Wavelet-based analysis of singularities in seismic data

Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science

Reexamination Certificate

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Reexamination Certificate

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06745129

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates specifically to anew seismic attribute for seismic trace analysis.
More specifically, the present invention relates to application of a wavelet transform to seismic traces and subsequent calculation of pointewise Hölder exponents at local time points. The resulting data can be used to produce greatly improved seismic imaging of stratigraphic features in 2D, 3D or time lapse data. The Hölder exponents correlate closely with results from acoustic impedance and therefore allow data gathering to provide new geophysical information. In addition, Hölder exponents calculated from singularities in the traces very sharply and distinctly delineate borders between strata.
2. Prior Art
In the petroleum industry, drilling risk is best reduced by accurate, reliable imaging of geological subsurface formations. Stratigraphic and geophysical data is vital to finding petroleum reservoirs. A rapidly expanding area of oil exploration is in the image analysis of seismic trace data.
In order to obtain a view of the subsurface geology, explorationists generate seismic waves and use sensors to detect reflections of these waves. Interfaces in the subsurface strata reflect these waves. By measuring the returned amplitude and the time between initiation of the wave and its reflection to the surface, subsurface features may be detected. Measurements made by the sensors are known as seismic traces.
A variety of methods have been developed to reduce noise and increase resolution. Traces resulting from identical points in the subsurface may be stacked (summed) and migrated. This both reduces noise and increases resolution. However, coherent noise tends to be amplified by trace stacking. Fourier transforms may also be applied to seismic traces in an effort to identify and eliminate coherent noise. There have been many attempts to improve interpretation of seismic trace data, none of which contemplate the methodology of the present invention.
The patent (U.S. Pat. No. 5,563,949) to Bahorich et al. (1996) and the patent (U.S. Pat. No. 6,092,025) to Neff(2000) fall into the category of a new seismic attribute from seismic data. The attribute from U.S. Pat. No. 5,563,949, coherency cube, is from similarity or correlation analysis of adjacent seismic traces and gives better standout of geological structures such as faults or edges of salt domes. However, the nature of their algorithm makes their attribute useful primarily only on horizontal or time slices and all relevant stratigraphic information on vertical sections are smeared out. There is no application of a wavelet transform.
U.S. Pat. No. 6,092,025 calculates a type of seismic attribute called Delta Amplitude Dip (DAD), also based on similarity or cross-correlation analysis of adjacent seismic traces, and claims that displaying DAD values in a time or horizontal slice provides a direct indicator of hydrocarbon. Cross-correlation analysis is good for edge detection, and thus both coherency cube and DAD are suitable for detecting faults or edges in seismic data. At the same time they also share the same kind of drawbacks (DAD attribute is also only suitable for time or horizontal slice). In contrast, the algorithm of the present invention, based on the wavelet transform, is highly localized and gives clearer images of subsurface singularity variations, and therefore is able to deliver stratigraphic and structural information from both vertical and horizontal sections.
One important feature of the present invention is the application of wavelet transform and the calculation of Hölder exponent/singularity analysis on seismic data. None of these operations or analyses was performed in reported patents. However, the algorithm named very short-time Fourier transform (VSTFT), in the invention (U.S. Pat. No. 5,850,622) of Vassiliou et al., (1998) is similar to ours. Vassiliou et al. claimed that by using VSTFT, shorter in length than was felt to be possible, they discovered a novel method of reorganizing the data in frequency domain so that noise can be better removed or attenuated, and attribute analysis and trance editing becomes easier. Their method gives no clue as to how singularity analysis can be performed.
As we all know, wavelet transform is an outgrowth of Fourier transform or short-time Fourier transform, but what makes the wavelet transform more powerful is that it is localized in both frequency and time domain. The Short-time Fourier transform tries to gain some time resolution with a short, fixed-length time window. Unlike the wavelet transform which uses variable-length windows, a fixed window will make the time resolution everywhere the same. According to the Reciprocal Uncertainty Principle, a very short time window, as implemented by Vassiliou et al., gives better time resolution but poor frequency resolution, which makes localized frequency analysis very impractical.
Several inventions (U.S. Pat. No. 4,679,174 to Gelfand; U.S. Pat. No. 4, 817,062 to De Buyl et al.; U.S. Pat. No. 5,487,001 and U.S. Pat. No. 6,092,025 to Neff) are focused on combining geologic information, such as well logs, with seismic data for extracting subsurface lithologic information. These generally involve modeling and inversion. These inventions are unlike the present invention in terms of research goal and methodology. U.S. Pat. No. 4,679,174 (Gelfand) basically explores the power of forward modeling and iteratively compares seismic synthetics with real seismic data until an acceptable match has been achieved. Gelfand attempts to use modeling to recover high-frequency components in the seismic spectrum that are lost during seismic exploration. The present invention does not try to recover high-frequency components (fine scale stratigraphic information) lost in the seismic trace. Rather, it focuses on delineating locations and singularity strengths of stratigraphic boundaries. The present invention creates an image showing a new attribute in which stratigraphic information is more prominent than in seismic amplitude images (where the nature of seismic reflectivity can obscure true acoustic impedance variations). Even though seismic data are band-limited, the amplitude spectrum for a typical seismic trace still reveals a frequency band of ~10 to 100 Hz, which is broad enough for a successful multiscale analysis based on continuous wavelet transform. Since no inverse wavelet transform is necessary for singularity analysis, the invention disclosed herein simply analyzes the scaling information within the seismic frequency band.
U.S. Pat. No. 4,817,062 (De Buyl et al., 1989) applies similar data sets and concepts as U.S. Pat. No. 4,679,174 (Gelfand). In U.S. Pat. No. 4,817,062 a method for estimating subsurface porosity is established based on integration of well and seismic data. First, a seismic acoustic model is obtained from seismic survey. Then porosity information is assessed based on a porosity-acoustic impedance relationship they derived in the invention. Both the goal and the methodology of De Buyl et al. are quite different from ours.
Two patents to Neff (U.S. Pat. No. 5,487,001 and U.S. Pat. No. 6,092,025) disclose estimating subsurface petrophysical properties from the integration of seismic and well data. They are very similar in concept, both applying forward modeling, synthetic generation, iterative comparison between synthetics and real seismic traces. U.S. Pat. No. 5,487,001 (1996) is designed to determine petrophysical properties associated with a subterranean layer, while patent U.S. Pat. No. 6,092,025 (1998) is more tuned for estimating vertical permeability and porosity variations within a reservoir. Neither utilizes the wavelet transform and thus distinct from the present invention.
The patent (U.S. Pat. No. 6,246,963 B1) to Cross et al. (2001) involves no seismic data but quantitative stratigraphic modeling to predict stratigraphic and sedimentologic attributes at locations other than those at which data are collected. Clearly this invention falls into the category of m

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