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Results
High-Resolution Geological Visualization Using Spectral Inversion
Dwan, Fa (Shell E&P Technology) | Griffiths, Don (Shell E&P Co.) | Gil, Jose (Shell Venezuela Co) | Portniaguine, Oleg (Fusion Petroleum Technologies) | Moreno, Carlos (Fusion Petroleum Technologies) | Burnett, Mike (Fusion Petroleum Technologies) | Mastrangelo, Carlos (Fusion Petroleum Technologies) | Castagna, John (U. of Houston)
Abstract Conventional seismic inversion operates in the time domain and outputs relative impedances. Spectral inversion (Portniaguine and Castagna, 2005; Partyka, 2005) operates in the frequency domain where the spectrum of a local seismic response is taken to be a superposition of sinusoidal transfer functions associated with reflection coefficient pairs. Thus, the output time domain reflectivity series is a superposition of odd and even impulse functions. Widess (1957; reprinted in 1973) showed that odd impulse functions have a resolution limit of about 1/8th of a wavelength, below which the response is approximately the derivative of the seismic wavelet, irrespective of layer thickness. As an odd impulse pair thins below this limit, the peak frequency of the response remains relatively constant while the amplitude decreases almost linearly with thickness. According to this model, for layer thickness below 1/8th of a wavelength one cannot separate differences in reflection coefficient magnitude from changes in layer thickness (even in the absence of noise), and thus 1/8th of a wavelength is generally considered the limit of seismic resolution. However, for even impulse pairs, response frequency varies continuously with layer thickness and, in the absence of noise, layer thickness and reflection coefficient magnitude can both be precisely determined. Thus, for an isolated layer with an even component of reflectivity, resolution is nearly perfect in the absence of noise. Spectral inversion, by appropriately weighting odd and even components of reflection coefficient pairs, can thus achieve the best possible combination of resolution and robustness to noise. As is the case with all sparse-spike inversion methods, the output reflectivity series contains frequency components outside the band of the original seismic data. As the spectra of impulse pairs are sinusoids with infinite frequency content, in the absence of noise, all frequencies out to Nyquist can theoretically be recovered. In practice, in the presence of noise, the useful data bandwidth can often be increased by a factor of two or three. The broader the bandwidth of the original data, the more robust the process is against noise. By examining filter panels of the output reflectivity series, one can select the bandwidth that provides the most enhanced "spectrally broadened" image. Filtering back to the original bandwidth of the data reproduces the original data - the process is thus amplitude preserving and does not introduce "false" events. The spectral inversion method applied here requires no "starting model" and consequently makes no direct use of well information. The results are thus objective in the sense that they have not been biased by any interpretive input. Examples Case studies from Lake Maracaibo and the deep-water Gulf of Mexico illustrate the image improvement that results from such a process. It is important to note that in these case studies the only use of well control was in wavelet extraction and verification of results. No well information was incorporated into the inversion process. Figure 1 compares a conventional seismic section from Lake Maracaibo to a section spectrally broadened using spectral inversion. The increased frequency content (and corresponding improved resolution) after spectral broadening is obvious.
- South America > Venezuela > Zulia > Maracaibo (0.46)
- North America > United States > Texas (0.29)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.54)
The Vp/Vs Inversion Procedure: A Methodology for Shallow Water Flow (SWF) Prediction from Seismic Analysis of Multicomponent Data.
Moreno, Carlos (The University of Oklahoma) | Castagna, John (The University of Oklahoma) | Huffman, Alan (Fusion Petroleum Technologies) | Bertagne, Allen (PGS Reservoir Consultants, Inc.)
Abstract The petrophysical properties characteristic of the sands prone to shallow water flow (SWF) make them identifiable due to their abnormally high Vp/Vs ratios compared to the adjacent rocks. The Vp/Vs inversion procedure identifies anomalies that could be related to SWF sands and it is based on seismic inversion of multicomponent seismic data. The new approach for estimating the Vp/Vs ratio from multicomponent seismic data was evaluated on synthetic logs where the estimated Vp/Vs ratios matched very well the real values. For real ocean bottom cable data (OBC) from GOM, the Vp/Vs inversion procedure was used to estimate Vp/Vs sections that showed clear anomalies that could be related to sand bodies responsible for SWF. These sections are useful for identifying high Vp/Vs ratio zones and could be used to plan a drilling program to prevent SWF during perforation. Preliminary analyses indicate that the methodology has the potential of being a direct hydrocarbon indicator (DHI) as well. Introduction In the last few years prospecting for hydrocarbon in deepwater has increased enormously. With these new plays, new problems have emerged. Several articles have been written to describe the new "geohazards" in deep-water projects. The most dangerous geohazard during drilling operations is known as Shallow Water Flow (SWF). It has been defined as water flowing to the ocean floor on the outside of structural casing (Alberty, 2001). This flowing water can erode the structural support of the well and lead to casing buckling and subsequent casing failure. This flow path can also compromise wellbore integrity, which can result in the loss of well control. SWF typically occurs when overpressured sandy zones are penetrated and shallow casing is not bonded to the formation. When overpressured sands occur, the appropriate drilling technology should be used to prevent losses and future remediation requiring additional expenditures. However, the prediction of such sands is required to plan appropriately the option that will control and/or prevent shallow water flows. Interpretation of two-dimensional and three-dimensional high-resolution seismic data is becoming the most used technology for identification of geohazards. However, this method is not a direct indicator of SWF sands. It has been hypothesized (Huffman and Castagna, 2000) that unconsolidated sands that flow during drilling operations have abnormally high Vp/Vs ratios. The use of multicomponent seismic data, previously recorded for petroleum exploration purposes, is used in this project to recognize shallow Vp/Vs ratio anomalies. A methodology that can be used as a direct indicator of overpressured sands, and presents the derived high-resolution Vp/Vs sections for a test line located in deep water in the Gulf of Mexico is described. It has been developed to identify potential Shallow Water Flows sands, however, it could be implemented in hydrocarbon prospecting. This new methodology, based on the appropriate processing and interpretation of pre-stack and post-stack multicomponent seismic data has the potential to be a direct hydrocarbon indicator (DHI) tool more powerful than AVO analysis. We will hereafter refer to the methodology described as the "Vp/Vs Inversion Procedure", as it is designed to estimate the Vp/Vs ratio at the seismic scale.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.69)
- Geology > Geological Subdiscipline > Stratigraphy (0.47)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Multicomponent Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Seismic Reservoir Characterization > Direct Hydrocarbon Indicators (DHI) (0.94)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.68)