Communications – electrical: acoustic wave systems and devices – Seismic prospecting – Land-reflection type
Reexamination Certificate
2002-08-29
2004-06-29
Moskowitz, Nelson (Department: 3663)
Communications, electrical: acoustic wave systems and devices
Seismic prospecting
Land-reflection type
C367S038000, C367S040000, C367S043000, C367S045000, C367S050000, C367S070000, C702S014000, C702S017000
Reexamination Certificate
active
06757217
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to the field of geophysical prospecting. More particularly, the invention relates to the field of seismic data processing. Specifically, the invention is a method of time-aligning near-offset and far-offset seismic data volumes.
2. Description of the Related Art
The ability of geoscience interpreters to perform quantitative volumetric AVO (Amplitude Versus Offset) analysis for the exploration, development and production of hydrocarbon resources depends upon the accurate alignment between multiple offset seismic volumes. AVO analysis requires the simultaneous analysis of two or more pre-stack time offset or angle volumes and the resulting generation of accurate derivative AVO attribute volumes. The goal of the analysis is to quantify and discriminate the anomalies from the background trends. As large high-resolution 3D seismic surveys are now commonplace in industry, a timely, quantitative analysis is required to impact business decisions. Volume-based AVO analysis can add significant contributions to a geologic/geophysical understanding of a target, even in exploration and early development where well control is sparse. However, practical circumstances generally preclude full 3D pre-stack AVO inversion. In place of a full theoretical consideration, significant quantitative information can be gleaned from just near-offset and far-offset volumes. However, working with just two stacked offset volumes (effectively a two-point gather) requires a disciplined and deliberate workflow to ensure that the final products are geophysically realistic.
Even after application of a state of the art AVO processing stream, small differences in the alignment of stacked AVO seismic data volumes prevent accurate, direct (point-for-point) differencing of these data. This misalignment hinders the accurate calculation of three-dimensional volume-based seismic AVO attributes. There are two main techniques used in the oil industry to automate or enhance the calculation of AVO attributes. These techniques are (1) horizon-based quantitative AVO analysis and (2) a seismic inversion approach to either elastic impedance or Vp, Vs, and density in vendor software. In general, horizon based methods are more time consuming than volume-based methods. Examples of these techniques or variants of them are discussed next.
The software product GWB, by the vendor Jason, allows the user to grossly align multiple seismic volumes with multiple-horizon, window-based cross-correlations. This technique will allow for the gross alignment of volumes. Unless numerous horizons are used, this method will produce much less precise results than the new method disclosed herein. Additionally, GWB does not allow for sub-sample interpolation, or for retaining and exploiting the time shift and cross-correlation volumes for filtering or AVO analysis. The Jason approach requires well control and good Vp and Vs log data to facilitate the extraction of seismic wavelets.
U.S. Pat. No. 4,203,161 to Johnson, Parrack and Lunsford, discloses using a cross correlation metric to time-shift seismic trace segments. This process is done pre-stack and therefore is purely a processing technique. They do not discuss use in post-stack migration or for quantitative analysis.
Ratcliffe and Adler, “Accurate Velocity Analysis for Class II AVO Events”, discuss a method for better AVO velocity analysis for prestack data when class II AVO anomalies exist. (SEG 2000 Expanded Abstracts, SEG International Exposition and Seventieth Annual Meeting, Calgary, Alberta, Aug. 6-11, 2000). Their method evaluates the move out correction relative to the validity of the resulting AVO. This technique, however, can not be applied to near-and far-offset seismic data volumes.
Balz and Pivot, “Fast Identification of AVO Anomalies Using Classification of Pre-Stack Waveforms”, SEG 2000 Expanded Abstracts, Society of Exploration Geophysicists International Exposition and Seventieth Annual Meeting, Calgary, Alberta, Aug. 6-11, 2000, discuss a methodology using self-organising maps or &kgr; means clustering to classify AVO response. Their method is designed to work with pre-stack seismic data and for a specific interval defined through horizon interpretation. Their method is interval-based and not volume-based. There is no mention of building a 3D consistent time shift volume to time align AVO cubes, or exploiting multiple attributes for AVO classification, such as cross correlation and near and far product with difference. Additionally, they do not mention the use of a probabilistic neural network approach with user defined training.
Two publications, (1) Eastwood et al., “Processing for Robust Time-Lapse Seismic Analysis: Gulf of Mexico Example, Lena Field,” Society of Exploration Geophysicists, 1998 Annual Meeting and (2) Johnston, D., Eastwood, J. and Shyeh, J., “Seismic Monitoring Lena Gulf of Mexico”, The Leading Edge, April, 2000, disclose a rudimentary version of a cross-correlation algorithm for time lapse or 4D seismic applications. These publications discuss the use of the aligned volumes to help resolve residual migration and alignment issues between multiple legacy 3D surveys and to facilitate the differencing of 3D seismic surveys acquired through time. Additionally, two new 4D seismic attribute volumes were introduced, the time-shift volume and the cross correlation volume. However, this algorithm did not have the capability of interactive filtering of time shift and correlation volumes to improve the spatial and temporal difference. Furthermore, the algorithm did not have the dual window cross correlation capability or an iterative workflow approach. Finally, the use of the algorithm for AVO analysis was not discussed.
In their publication, “High-Fidelity Inverse Estimate of AVO Response”, Society of Exploration Geophysists, 69
th
Annual Meeting, Houston, Tex., 1999, Reilly et al. disclose a rule-based alignment method. This method selects all the peaks and troughs in the seismic traces of near offset and far offset seismic data volumes. Then, using the far offset volume as the frame of reference, the method searches for closest extremums in the near offset cube, limited by the far offset zero crossings or inflection points and other user-selected constraints. Once alignment has been achieved the maximum of the absolute value of either the far*(far-near) attribute or near*(far-near) attribute is retained. The output volume format is either a sparse spike reflection series or a blocked reflection series. Prior to differencing, relative amplitude scaling is achieved with a long-time-varying trace-to-trace gain equalization. This method, however, does not retain the seismic frequency content in the data. Furthermore, it is a single trace-to-trace operation with no volume based filtering, and no creation of time shift and correlation volumes and subsequent exploitation of these volumes to create a spatially and temporally consistent difference volume. Finally, the calculation is done at discrete sample intervals of ¼ of the SEGY sample rate.
Thus, a need exists for a method that improves the alignment of offset seismic data, enabling the calculation and manipulation of higher-resolution AVO data.
SUMMARY OF THE INVENTION
The invention is a method for time-aligning near-offset and far-offset seismic data volumes. In one embodiment, a plurality of time shifts are first selected. The near-offset and far-offset seismic data volumes are cross-correlated at the plurality of time shifts. An initial time-shift volume and a maximum correlation volume are created from the maximal cross-correlations at the plurality of time shifts. Areas of high time shift from the initial time-shift volume and areas of low cross-correlation from the maximum correlation volume are determined. The determined areas of high time shift and low cross-correlation are filtered from the initial time-shift volume, generating a filtered time-shift volume. Finally, the filtered time-shift volume is applied to the far
Dawson Dwight C.
Eastwood John E.
Gross Michael D.
Johnston David H.
West Brian P.
ExxonMobil Upstream Research Company
Moskowitz Nelson
Schweppe Charles R.
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