Seismic attributes for structural analysis

Embodiments of methods and systems for analysis of geological structures using seismic attributes are described. In some embodiments, a method includes computing a similarity function using one or more seismic attributes at a location within the geological structure along an I direction and a J direction; computing a total optimal similarity function in at least one plane defined by the I and J directions; computing a minimum possible value of the total similarity function for a defined range of rotations; and calculating a discontinuity measure based at least partially on the minimum possible value of the total similarity function.

FIELD OF THE INVENTION

The present disclosure relates to computational simulation and analysis of geological formations, and more specifically, to simulation and analysis of geological structures using seismic attributes.

BACKGROUND

Research operations, such as surveying, drilling, testing, and computational simulations, are typically performed to help locate and extract valuable hydrocarbon resources. The information developed during such research operations may be used to assess geological formations, and to locate the desired subterranean assets.

Seismic data are routinely and effectively used to estimate the structure of reservoir bodies. Processing of seismic data produces “seismic attributes” which may be effectively used to interpret the underlying geological structure. Typically, seismic attributes may be considered mathematical transformations on seismic data, and may include, for example, acoustic impedance, and velocity, reflection heterogeneity and instantaneous frequency, depth, dip angle, and azimuth angle. Conventional methods and systems for simulation and analysis of geological structures using seismic attributes, include those described, for example, in U.S. Pat. No. 7,082,368 issued to Nickel, U.S. Pat. No. 6,950,786 issued to Sonneland et al., U.S. Pat. No. 6,240,370 issued to Sonneland et al., and U.S. Pat. No. 5,444,619 issued to Hoskins et al., which patents are hereby incorporated by reference. Although desirable results have been achieved using such conventional techniques, there is room for improvement.

SUMMARY

The present disclosure relates to methods and systems for analysis of geological structures using seismic attributes. Embodiments of methods and systems in accordance with the teachings of the present disclosure may advantageously avoid drawbacks typical to many conventional analysis algorithms that rely on direct spatial correlation measure, thereby providing improved structural interpretations.

In some embodiments, a method in accordance with the present disclosure includes analyzing a geological structure based at least partially on one or more seismic attributes, including: computing a similarity function using one or more seismic attributes at a location within the geological structure along an I direction and a J direction; computing a total optimal similarity function in at least one plane defined by the I and J directions; computing a minimum possible value of the total similarity function for a defined range of rotations; and calculating a discontinuity measure based at least partially on the minimum possible value of the total similarity function.

This summary is merely intended to provide a brief synopsis of a possible implementation of, and possible aspects or advantages of, systems and methods in accordance with at least some embodiments of the present disclosure. This summary is further intended as merely an aid to the reader's understanding of such particular embodiments, and is not intended to define or limit other embodiments of systems and methods disclosed elsewhere herein.

DETAILED DESCRIPTION

This disclosure is directed to methods and systems for structural analysis of geological structures using seismic attributes. More specifically, embodiments of methods and systems in accordance with the teachings of the present disclosure may use novel three-dimensional (3D) seismic attributes for analysis of geological structures, including identification of terminations and other suitable analyses.

In at least some implementations, systems and methods in accordance with the present disclosure may advantageously overcome drawbacks typical to many other structural analysis algorithms based on direct spatial correlation measure. For example, such systems and methods may advantageously provide robust estimation of “dip angles” of subterranean reservoir layers, and may allow improved control over the estimation process via “user settings.” Additional advantages or aspects of systems and methods in accordance with the present disclosure will become apparent through review of the following detailed description.

Exemplary Environment

FIG. 1is a schematic view of an exemplary environment100which may be modeled in accordance with the teachings of the present disclosure. More specifically, in this embodiment, the environment (or oilfield)100includes a subterranean formation102containing a reservoir104therein. A seismic truck106performs a survey operation by producing one or more waves111(e.g. sonic waves, ultrasonic waves, electromagnetic waves, etc.) that may be used to generate seismic data regarding the subterranean formation102.

More specifically, as shown inFIG. 1, one or more waves111are emitted by a source110and reflect off one or more horizons114in an earth formation116. The reflected waves112are then received or detected by one or more sensors, such as geophone-receivers118or the like, situated on the surface. In at least some embodiments, the geophone-receivers118produce electrical output signals in response to the characteristics of the reflected waves112(e.g. amplitude, frequency, etc.), referred to as seismic data received120inFIG. 1.

The seismic data received120may be provided as input to a computer122(e.g. located in the seismic truck106or elsewhere). Responsive to the input data, the computer122may generate a seismic data output124which may be stored, transmitted, or further processed as desired, including by various analysis techniques in accordance with the teachings of the present disclosure.

Additional aspects of systems and methods of simulating geological structures using seismic attributes described in the following sections. It should be appreciated that the systems and methods described herein are merely exemplary, and are included for illustration purposes and should not be construed as limiting.

Exemplary Processes

Attribute analysis is generally known as a powerful tool for analysis of seismic data. Fault and termination identification is typically part of attribute analysis, especially when it comes to three-dimensional (3D) seismic data analysis. Many known and used methods of fault identification are based on the measures of coherency-type attributes, variance, stability of gradient estimations, etc. One major drawback of such conventional methods, however, is that they may not be able to distinguish uncorrelated noise and fault discontinuity, so interpretation is needed to extract real fault traces from just noisy areas. In at least some implementations described herein, a set of proposed attributes is described which does not suffer from such limitations.

FIG. 2illustrates an exemplary method150of gathering seismic data and corresponding seismic attributes in accordance with the teachings of the present disclosure. In this embodiment, a well152is located on an earth surface above a wellbore154which penetrates a geological formation. A plurality of seismic instruments156(e.g. geophones, etc.) located on the earth surface emit signals into the geological formation which intersect a subsurface layer (or location)158at a corresponding plurality of points A, B, C, D.

InFIG. 2, point A on the subsurface layer158has a seismic trace160which includes an amplitude variation161, the amplitude variation161having an amplitude a1and a frequency f1. Similarly, points B, C, and D on the subsurface layer158have seismic traces162,164,166, respectively, each seismic trace having a corresponding amplitude variation163,165,167, respectively, each amplitude variation having a corresponding amplitude a3, a5, a7, and a corresponding frequency f3, f5, f7, respectively.

As further shown inFIG. 2(generally at134), for point A on the subsurface layer158, one attribute170associated with point A is amplitude a1, and another attribute171associated with point A is frequency f1. Similarly, for point B, one attribute172associated with point B is amplitude a3, and another attribute173associated with point B is frequency f3. For point C, one attribute174is amplitude a5and another attribute175is frequency f5. And for point D, one attribute176is amplitude a7and another attribute177is frequency f7.

However, a seismic trace having an amplitude variation may also be associated with the wellbore154. That is, inFIG. 2, a seismic trace168having an amplitude variation169is associated with the wellbore154, the amplitude variation169having an amplitude a9and a frequency f9. Therefore, for the wellbore154, one attribute178associated with the wellbore154is amplitude a9and another attribute179associated with the wellbore154is frequency f9. More specifically, the attributes178,179associated with the wellbore154may be considered “synthesized” attributes because the data obtained from the wellbore154are not seismic data, and from the non-seismic wellbore data, the seismic trace168may be “synthesized” and attributes178,179generated.

FIG. 3is an embodiment of a method190of analyzing a geological structure using seismic attributes in accordance with the teachings of the present disclosure. In this simplified embodiment, the method190includes obtaining seismic data at192(e.g. using a seismic system as described above with respect toFIG. 1, or retrieving existing data from storage), and performing analysis of a geological structure using a geological modeling component at194. It will be appreciated that a variety of suitable components for simulation and analysis of geological structures are known, including, for example, the SEISCLASS software product owned by Schlumberger Technology Corporation, or similar components for geological analysis owned by or available from Roxar Software Solutions, Inc., Quantitative Geosciences, Inc., Chevron, and many others.

As further shown inFIG. 3, performance of an analysis of a geological structure using a geological modeling component at194may include computing seismic attributes at196. Novel methods for computing seismic attributes (e.g. at196) in accordance with the teachings of the present disclosure are described more fully below. Finally, the method190includes continuing (e.g. iteratively returning to perform additional analyses at194), or terminating at198.

It should be appreciated that the computation of seismic attributes may be performed as part of the analysis of the geological structure at194, as shown inFIG. 3, or may be performed separately, such as prior to initiating the analysis of the geological structure. Therefore, the method190shown inFIG. 3is merely exemplary of a possible embodiment, and systems and methods in accordance with the present disclosure should not be construed as being limited to the particular embodiment shown inFIG. 3.

To begin to describe computation of seismic attributes in accordance with the teachings of the present disclosure, several definitions will now be introduced. For example, as used herein, a template Ω denotes an array of points ω with integer Cartesian coordinates (i,j,k) selected according to some custom rule, as shown in the following Equation (1):
Ω={ω1, . . . , ωn},ωm=(im,jm,km)  (1)

Similarly, as used herein, a sample Λ extracted around kernel point K using a template with parameters (φ,θ,ψ) denotes an array of λ, defined by the following Equation (2):
ΛΩK0(K,φ,θ,ψ)=(λ1, . . . , λn),λm=S(K+ωmφ,θ,ψ)  (2),
where S(t) denotes interpolated value of 3D seismic volume at point t with Cartesian coordinates (x,y,z), indexes φ,θ,ψ at point ω mean sequential rotation of applied template Ω to angle φ around Z axis and to angle θ around axis X, and to angle ψ around axis Y (or any suitable combination or ordering thereof) and K0is zero point of coordinate system where template rotation is performed.

Now, using notation introduced above it is possible to define a new measure of distance between two arbitrary points K1and K2in a seismic volume, referred to herein as a sample-based distance, as shown in the following Equation (3):

L⁡(ΛΩ⁡(K1,φ1,θ1,ψ1),ΛΩ⁡(K2,φ2,θ2,ψ2))=ΛΩ⁡(K1,φ1,θ1,ψ1)-ΛΩ⁡(K2,φ2,θ2,ψ2)ΛΩ⁡(K1,φ1,θ1,ψ1)+ΛΩ⁡(K2,φ2,θ2,ψ2)(3)
This measure allows a defining degree of similarity between different points in seismic volume in terms of selected template and angles. For example, a simple one-point template Ω={(0,0,0)} can lead to direct comparison of seismic amplitudes in locations K1and K2.

It will be appreciated that, in at least some implementations, methods and systems in accordance with the present disclosure are not based on explicit calculations of spatial correlation of the seismic signal. Instead, such methods and systems may be based on fundamental differences between main classes of areas in a seismic signal. For example, in some implementations, the classes may be designated as follows: areas without any discontinuities, faults or other termination areas with one or more discontinuities along a particular surface, and chaotic areas with many isotropic discontinuities. Listed classes may not just be theoretical, and in nature, areas of seismic data may include mixtures of these class types. However, if it will be possible to construct a robust measure of every point belonging to a fault class, such measure may advantageously be used as a fault indicator.

In some implementations, the characteristics of (or differences between) reservoir classes or zones may be described in terms of a correlation between samples. As used herein, the term “correlation between samples” may be interpreted as a small sample-based distance between points along a selected direction. In at least some implementations, three main classes or zones may be described as follows:

Fault area: Good correlation between samples along only one particular direction in the fault surface (“main direction”). Bad or no correlation between samples along any direction perpendicular to the main direction.

Chaotic area: No “main direction”, bad or no correlation between samples along any direction.

Area without discontinuities: Good correlation between samples along any direction in a particular plane (defined by local dip angle and dip azimuth). No “main direction”.

In addition, a generalized template {circumflex over (Ω)} can be defined using a simple template Ω given by Equation (4) below:

where τ represents a width of generalization, and coefficients η are equal to 1 or 0 depending on the presence of point (i,j,0) in a generalization. Removing part of the points using these coefficients can be performed for increasing the algorithm's performance. In some implementations, if performance is relatively less important, it may be possible to set η=1.

The generalized sample then may be defined using Equation (5) as follows:

It will be appreciated that rotations (φ,θ,ψ) can be applied to the corresponding general template, i.e. zero of local coordinate system can be defined by a kernel of template without shift (K).

Measures of similarity between kernels along I and J directions in a general sample may be introduced using Equations (6) and (7) as follows:

Weights μ are defined based on an actual form of function to be used. A simple case with generalization width equal to I is described in the following example. Consider numeration of individual templates in a generalized template (having width equal to 1) in accordance with a possible embodiment as shown in Diagram A below.

In this embodiment, possible sets of coefficients μ may be given by Equations (8) and (9) as follows:

Applying such μIleads to a similarity function equal to a sum of distances between all points with coordinate difference=(1,0,0) or (−1,0,0) (i.e. between points having a coordinate difference of one unit along one direction and zero along other directions). Similarity functions along I and J directions (Equations (6) and (7) above) may be used to define a total optimal similarity function G in each possible IJ plane, as given by Equations (10) through (13) below:

As seen from the above similarity function G(K), in at least some implementations, an arbitrary point K can define a minimum possible value of a total similarity function for a defined range of rotations. The similarity function G(K) may be used as a discontinuity measure since its values are low if there is a particular plane with good correlation of values along it. At the same time, the similarity function G(K) may have relatively high values in fault zones, and relatively higher values in chaotic regions without any spatial correlation of values. It will be appreciated that for each of the functions represented above, a different template (with different geometry, etc.) may be used, which may thereby provide improved quality of interpretation results.

To separate areas or zones within a geological structure (e.g. a fault zone from a chaotic zone), another measure can be used, as given by Equation (14) below:
G*(K)=(SimI(K,φ,θ,ψ)−SimJ(K,φ,θ,ψ)2(14)

where (φ*,θ*,ψ*) is the minimum's argument in the definition of the similarity function G(K). In at least some implementations, this measure is based on the above-referenced unique characteristic of the fault area, specifically, a relatively high anisotropy of points in terms of the sample-based distance D. After a minimum of the sample-based distance D is reached in (φ*,θ*, ψ*), the template may be oriented for best fit to the main direction in the fault area (e.g. re-orienting a template defining the I and J directions to provide an improved alignment of at least one of the I direction or the J direction with a main direction in a fault area). In isotropic areas, the similarity function G*(K) can have low values since both similarities along I and J directions in the best fit plane can be the same order of magnitude. At the same time, in the fault area one direction could be close to the main and, consequently, have a smaller value of similarity than another.

Therefore, in accordance with at least some implementations, the measure of the similarity function G*(K) (Equation (14) above) at every point K in a seismic volume could be used as a reliable indicator of a fault zone.

In addition, in further implementations, some improvements can be implemented to enhance contrast between a main direction and other directions around the fault zone. For example, in at least some implementations, an angle decomposition can be used to reduce computational times (or increase the speed of computations). Specifically, this may be accomplished by decomposing the minimization of the similarity function G(K) into two parts, given by Equations (15) and (16) below:

It will be appreciated that, in some implementations, the minimization of the similarity function G(K) which enables the valuation at (φ*,θ*,ψ*) of the similarity function G*(K) (where (φ*,θ*,ψ*) is the minimum's argument as shown in Equation (14)), this calculation can provide important estimations for local dip and azimuth angles. Thus, unlike other methods for estimation of the local dip and azimuth angles, the approach described herein may have user-defined settings, and therefore allows improved control over an estimation's neighborhood size.

From the description above, a variety of processes for analyzing a geological structure based at least partially on one or more seismic attributes may be implemented. For example,FIG. 4shows a flowchart of another embodiment of a process200in accordance with the teachings of the present disclosure. In this embodiment, the process200includes computing a similarity function using one or more seismic attributes at a location within the geological structure along an I direction and a J direction at202. For example, in at least some implementations, the computation of the similarity function at202may include computing a similarity function substantially using at least one of Equations (6) and (7) shown above.

As further shown inFIG. 4, in this embodiment, the process200includes computing a total optimal similarity function in at least one plane defined by the I and J directions at204. For example, in at least some implementations, the computing of the total optimal similarity function at204may include computing a total optimal similarity function G in one or more IJ planes, as given by Equations (10) through (13) shown above.

In the embodiment shown inFIG. 4, the process200further includes computing a minimum possible value of the total similarity function for a defined range of rotations at206, and calculating a discontinuity measure based at least partially on the minimum possible value of the total similarity function at208. At210, the process200includes performing analytical calculations, including, for example, analyzing a geological structure based at least partially on one or more seismic attributes. At212, the process200determines whether the desired analytical calculations are complete. If not, the process200returns and iteratively repeats the activities202through208. Otherwise, the process200may terminate or continue to other activities at214. It will be appreciated that one or more of the activities of the processes described herein, including the processes and activities described above with respect toFIGS. 1 through 4, may either be tied to a particular apparatus, or may involve a transformation of something (e.g. data, information, etc.) into a different state or thing.

From the foregoing detailed description, it will be appreciated that embodiments of methods and systems in accordance with the teachings of the present disclosure may provide considerable advantages over conventional techniques. Several examples of how such embodiments may be used are described below, however, these brief examples are merely representative and should not be construed to in any way limit the functionality or applicability of the methods and systems described herein, or the scope of the claims listed below.

For example, in one or more implementations, embodiments in accordance with the present disclosure may not have an inherent limitation of other methods, namely, that of high values of resulting attributes in noisy areas. In at least some implementations, by overcoming this limitation of at least some prior art methods, improved analyses of geological structures may be achieved.

Similarly, in at least some implementations, embodiments in accordance with the present disclosure may allow identification of both large and small discontinuities. In some aspects, user-specified settings may provide improved control over a desired (or required) refinement of the analysis.

Embodiments of methods and systems in accordance with the present disclosure may also not be based on any assumptions about geometrical shape of faults, or termination or reservoir layers. Such embodiments may therefore be applied to a wide range of available data. In addition, in at least some implementations, along with the identification of faults and terminations, embodiments in accordance with the present disclosure may provide robust estimation of reservoir dip and azimuth angles.

Exemplary Computational Environment

Systems and methods for analysis of geological structures using seismic attributes in accordance with the teachings of the present disclosure may be implemented in a variety of computational environments. For example,FIG. 5illustrates an exemplary environment250in which various embodiments of systems and methods in accordance with the teachings of the present disclosure can be implemented. In this implementation, the environment250includes a computing device260configured in accordance with the teachings of the present disclosure. In some embodiments, the computing device260may include one or more processors262and one or more input/output (I/O) devices264coupled to a memory270by a bus266. One or more Application Specific Integrated Circuits (ASICs)265may be coupled to the bus266and configured to perform one or more desired functionalities described herein.

The one or more processors262and/or the one or more ASICs265may be composed of any suitable combination of hardware, software, or firmware to provide the desired functionality described herein. Similarly, the I/O devices264may include any suitable I/O devices, including, for example, a keyboard264A, a cursor control device (e.g. mouse264B), a display device (or monitor)264C, a microphone, a scanner, a speaker, a printer, a network card, or any other suitable I/O device. In some embodiments, one or more of the I/O components264may be configured to operatively communicate with one or more external networks290, such as a cellular telephone network, a satellite network, an information network (e.g. Internet, intranet, cellular network, cable network, fiber optic network, LAN, WAN, etc.), an infrared or radio wave communication network, or any other suitable network. The system bus266of the computing device260may represent any of the several types of bus structures (or combinations of bus structures), including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.

The memory270may include one or more computer-readable media configured to store data and/or program modules for implementing the techniques disclosed herein. For example, the memory270may host (or store) a basic input/output system (BIOS)272, an operating system274, one or more application programs276, and program data278that can be accessed by the one or more processors262for performing various functions disclosed herein.

The computing device260may further include a structural interpretation package300in accordance with the teachings of the present disclosure. More specifically, the structural interpretation package300may be configured to perform analysis of a geological structure310using seismic attributes, including, for example, those processes and activities described above (e.g. as shown and described with respect toFIGS. 3 and 4).

As depicted inFIG. 5, the structural interpretation package300may be stored within (or hosted by) the memory270. In alternate implementations, however, the structural interpretation package300may reside within or be distributed among one or more other components or portions of the computing device260. For example, in some implementations, one or more aspects of the structural interpretation functionalities described herein may reside in one or more of the processors262, the I/O devices264, the ASICs265, or the memory270(e.g. one or more application programs276).

“Computer storage media” include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media may include, but is not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory technology, compact disk ROM (CD-ROM), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium, including paper, punch cards and the like, which can be used to store the desired information and which can be accessed by the computing device260. Combinations of any of the above should also be included within the scope of computer readable media.

Moreover, the computer-readable media included in the system memory220can be any available media that can be accessed by the computing device260, including removable computer storage media (e.g. CD-ROM220A) or non-removable storage media.

Computer storage media may include both volatile and nonvolatile media (or persistent and non-persistent) implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Generally, program modules executed on the computing device260may include routines, programs, objects, components, data structures, etc., for performing particular tasks or implementing particular abstract data types. These program modules and the like may be executed as a native code or may be downloaded and executed such as in a virtual machine or other just-in-time compilation execution environments. Typically, the functionality of the program modules may be combined or distributed as desired in various implementations.

Referring again toFIG. 5, it will be appreciated that the computing device260is merely exemplary, and represents only one example of many possible environments (e.g. computing devices, architectures, etc.) that are suitable for use in accordance with the teachings of the present disclosure. Therefore, the computing device260shown inFIG. 5is not intended to suggest any limitation as to scope of use or functionality of the computing device and/or its possible architectures. Neither should computing device260be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the example computing device260.

“Computer storage media” include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.

Conclusion

Although embodiments of systems and methods for automated structural interpretation have been described in language specific to structural features and/or methods, it is to be understood that the subject of the appended claims is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as exemplary implementations of automated structural interpretation.