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Collaborating Authors
Castagna, John P.
Summary As a seismic wave propagates, it loses energy due to spherical divergence, scattering, intrinsic absorption and reflection at interfaces where rock properties change. The amplitude and frequency responses of the reflected seismic wave are influenced by a variety of factors including: geologic structure, layer thickness, lithology, and pore fluid properties. When the seismic wave travels back to the surface, it also bring back the information related to stratigraphic features, rock property changes and hydrocarbon accumulations. Each reservoir has its own characteristic seismic frequency response because of its unique rock and fluid properties discriminating it from the surrounding environment. We utilize a spectral decomposition method to extract the characteristic frequency components from seismic data and identify low frequency anomalies. To understand the underlying physical factors of the low frequency anomaly, we build a set of wave-equation based synthetic forward modeling. The result of our analysis shows that seismic waves travel more slowly through gas zone than the background material is a main reason for seismic time series delay and low frequency anomaly in the thin layer reservoir. Our explanation has been applied in the analysis of frequency anomalies corresponding to gas-bearing sands in the Gulf of Mexico fields. Introduction Low frequency energy anomalies associated with reservoirs have been observed for many years. Taner et al. (1979) noted the occurrence of lower frequencies beneath gas and condensate reservoirs. Castagna et al. (2003) showed that gas reservoirs could be identified by low-frequency shadows. Li (2006) presented a method using the continuous wavelet transform to detect thick gas reservoirs. So far, there are no proven explanations for the low-frequency phenomenon. Many researchers applied attenuation concept to justify low frequency, because attenuation is like a low pass filter, it suppresses higher frequencies proportionally more than the lower frequencies, some oil /gas reservoir place, gas containing targets usually own a lower Q value than its background and exhibit a zone of anomalous absorption lying in a larger background region (Winkler and Nur 1982; Klimentos, 1995; Parra and Hackert, 2002; Kumar et al 2003). Yet, it is often difficult to explain observed shadows under thin reservoirs where there is insufficient travel path through absorbing gas reservoir to justify the observed shift of spectral energy from high to low frequencies (Castagna 2003). If low frequency anomaly was caused by pure attenuation factors, we can compensate the high-frequency components within that zone by applying a reverse Q filter. But, Yanghua Wang (2007) showed the low-frequency shadow zone still exists even after Q compensation. The purpose of this paper is to analyze the mechanisms that influence local frequency components of seismic data in thinlayers (half-wavelength thickness). A detailed forward model is built to help for understanding the underlying physical factors and evaluation of the contributions of various factors (related to local fluid properties, lithology change and layer thickness variation) to local frequency anomalies. The result of our analysis shows that seismic signal travels in gas /oil zone at low velocities that in turn result in push down of reflectors and cause the delay in time series.
- North America > United States (0.24)
- North America > Mexico (0.24)
- Geology > Rock Type (0.91)
- Geology > Geological Subdiscipline (0.90)
Comparison of Frequency Attributes From CWT And MPD Spectral Decompositions of a Complex Turbidite Channel Model
Castagna, John P. (Department of Geosciences) | Tai, Shenghong (Department of Geosciences) | Puryear, Charles I. (Department of Geosciences) | Dwan, Fa (Shell International Exploration and Production Company) | Masters, Ron (Shell International Exploration and Production Company)
Summary Various studies have demonstrated the usefulness of spectral decomposition and its associated frequency attributes in seismic interpretation and hydrocarbon exploration. However, many different techniques for spectral decomposition exist in the petroleum industry, creating a need for comparative studies of these techniques to evaluate their utility. In this work, we compare the results of the application of the CWT and MPD algorithms and associated frequency attributes to a complex turbidite model. Our results indicate that better resolution of stratigraphic features is achieved by the MPD algorithm. These improvements include sharper definition of lateral stratigraphic changes and detection of subtle channel features associated with off-peak frequencies. We also show the effective extraction of stratigraphic features associated with off-peak frequencies achieved by principal component analysis. We believe a quantitative assessment of the relationship between the rock properties volume and frequency attributes will provide useful insight during future work. Introduction Spectral decomposition is a seismic analysis technique that decomposes seismic data into the time-frequency domain, which often contains useful information for layer thickness estimation (Partyka et al.,1999; Puryear and Castagna, 2008), stratigraphic interpretation (Marfurt and Kirlin, 2001; Puryear and Castagna, 2008), and hydrocarbon indication (Castagna et al., 2003; Sinha et al., 2005). There are many spectral decomposition algorithms and frequency attributes that can be generated from spectral decomposition volumes. In this paper, we compare results obtained from two common spectral decomposition algorithms – the Continuous Wavelet Transform (CWT) and Matching Pursuit Decomposition (MPD). Because of the large volume of data produced by the spectral decomposition process, the general objective of frequency attributes applied to spectral decomposition is to reduce the quantity of cumbersome frequency volumes to a manageable number while retaining the most geologically pertinent information contained within the redundant frequency volumes. Common frequency attributes include peak frequency/peak amplitude mapping (Marfurt and Kirlin, 2001) and principal component analysis of spectral components (Guo et al., 2006). Our objective is to compare both the spectral decomposition results generated by the CWT and MPD and the frequency attributes derived from those results. We apply the algorithms to synthetic data generated by the application of the 3D “Huygens” method to a complex turbidite rock properties model (van Hoek and Salomon, 2006). Theory and Method The CWT is a commonly-used wavelet transform that utilizes orthogonal basis wavelets in order to decompose the seismic trace into individual frequency components. The CWT is essentially equivalent to a narrow-band filtering of the data in the temporal domain. We apply CWT to seismic traces using a Morlet wavelet basis function, which utilizes a window that varies as a function of frequency. MPD is a technique for time-frequency analysis that utilizes non-orthogonal basis functions, thereby allowing for atoms with more time compactness and more flexibility in the selection of atoms that match the shape of the trace. We compare the results from the spectral decompositions directly and then use these results as input into peak frequency/peak amplitude mapping and principal components mapping for comparison. Peak frequency/peak amplitude mapping tracks the frequency with the highest amplitude and the amplitude at that frequency along a particular horizon.
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment > Deep Water Marine Environment (0.83)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.83)
Spectral inversion is a seismic method that uses a priori information and spectral decomposition to improve images of thin layers whose thicknesses are below the tuning thickness. We formulate a method to invert frequency spectra for layer thickness and apply it to synthetic and real data using complex spectral analysis. Absolute layer thicknesses significantly below the seismic tuning thickness can be determined robustly in this manner without amplitude calibration. We extend our method to encompass a generalized reflectivity series represented by a summation of impulse pairs. Application of our spectral inversion to seismic data sets from the Gulf of Mexico results in reliable well ties to seismic data, accurate prediction of layer thickness to less than half the tuning thickness, and improved imaging of subtle stratigraphic features. Comparisons between well ties for spectrally inverted data and ties for conventional seismic data illustrate the superior resolution of the former. Several stratigraphic examples illustrate the various destructive effects of the wavelet, including creating illusory geologic information, such as false stratigraphic truncations that are related to lateral changes in rock properties, and masking geologic information, such as updip limits of thin layers. We conclude that data that are inverted spectrally on a trace-by-trace basis show greater bedding continuity than do the original seismic data, suggesting that wavelet side-lobe interference produces false bedding discontinuities.
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.47)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Well Tie (0.68)
AVO (amplitude variation with offset) analysis was formerly introduced by Bill Ostrander (in a presentation at the 1982 SEG Annual Meeting), for confirmation of bright spots and other anomalous reflections seen on RAP (relative-amplitude preserved) sections. Since then, we have come a long way in extracting more information from prestack seismic amplitudes. A few crucial points that have emerged in all these analyses are: (1) an understanding of the assumptions underlying the theory being put to use and how well these assumptions are met in practice, (2) the realization that AVO reliability strongly depends on the quality of the processing, and (3) awareness of the importance that correct interpretation techniques be employed to extract meaningful information from the AVO attributes.
- North America > United States > Colorado > Piceance Basin > Rulison Field > Mesaverde Formation (0.99)
- North America > United States > California > Sacramento Basin (0.99)
- North America > Trinidad and Tobago > Trinidad > North Atlantic Ocean > Columbus Basin (0.99)
ABSTRACT An algorithm for the calculation of bed thickness, reflection coefficients, and time location is described and applied. The algorithm is derived from the amplitude spectrum and has been tested on thin beds below one-quarter wave-length with variable reflection coefficient ratios. While the results have been very encouraging thus far, further testing is warranted.
ABSTRACT Seismic attenuation measurements from surface seismic data using spectral ratios are particularly sensitive to inaccurate spectral estimation. Spectral ratios of Fourier spectral estimates are subject to inaccuracies due to windowing effects, noise, and spectral nulls caused by interfering reflectors. We have found that spectral ratios obtained using continuous wavelet transforms as compared to Fourier ratios are more accurate, less subject to windowing problems, and more robust in the presence of noise.
Scale Attributes From Continuous Wavelet Transform
Sinha, Satish K. (School of Geology and Geophysics, University of Oklahoma, Norman, OK) | Routh, Partha S. (Dept. of Geosciences, Boise State University, Boise, ID) | Anno, Phil D. (Seismic Imaging and Prediction, ConocoPhillips., Houston, TX) | Castagna, John P. (University of Houston, Houston, TX.)
Summary Average instantaneous attributes of time-frequency decompositions are useful in revealing the time varying spectral properties of seismic data. In the continuous wavelet transform (CWT), a time signal is decomposed into a time-scale spectrum or a scalogram; unlike a timefrequency spectrum or a spectrogram from the short time Fourier transform (STFT). Although there are various approaches of converting a time-scale spectrum into a timefrequency spectrum we introduce new mathematical formulas to calculate spectral attributes from the scalogram. In this process, we bypass the conversion of a scalogram into a time-frequency spectrum and provide average spectral attributes based on scale. The attributes are: center frequency, dominant frequency, and spectral bandwidth. Since these attributes are based on the CWT, computation of these attributes avoids subjective choice of a window length. Introduction Spectral decomposition of non-stationary signals, like seismic signals, is conventionally achieved by the STFT. Therefore, average instantaneous spectral attributes, such as center frequency, dominant frequency etc., from a spectrogram inherits the properties of the STFT. A relatively new approach of spectral decomposition based on the CWT avoids the subjective choice of a window length and produces a time-scale spectrum also called scalogram. Sinha et al. (2003) converted a scalogram into a timefrequency spectrum called the time-frequency from CWT (TFCWT). One can use the TFCWT to compute instantaneous spectral attributes using the formulas outlined by Barnes (1993). However, we define new formulas to compute the instantaneous spectral attributes directly from a scalogram. In this definition we utilize the fact that the dilating support of the given wavelet, i.e. scale, is inversely proportional to the center frequency of the wavelet (Abry et al., 1993). We use Morlet wavelet for which this mapping is a good approximation. Theory Instantaneous spectral attributes of center frequency, dominant frequency, and spectral bandwidth are defined as various moments of a time-frequency distribution using familiar definitions from probability theory (Barnes, 1993). Example Comparisons of instantaneous spectral attributes based on frequency and scale for a seismic trace (Figure 1a) are shown in Figures 1-b, c, and d. We note the remarkable similarity between these attributes although the attributes computed from the scalogram seems to be smoother. This is expected because a scale represents a band of frequency and not a single frequency. Therefore, overlap of frequencies from one scale to another produces smoother attributes, but the overall agreement is excellent. The favorable comparison is a consequence of the scale to center frequency mapping for a Morlet wavelet. Computation of attributes based on scalogram is much faster than those based on TFCWT. Therefore, from practical point of view, attribute definitions based on scale (equations 6, 7, and 8) are computationally efficient at the cost of slight smoother representation of the information. This efficiency translates to saving considerable amount of time depending upon length and number of traces in a large 3D data set. The next logical step is to compare results on a real seismic section and examine if there are any difference in interpretation.
ABSTRACT This paper presents a new methodology for computing a time-frequency map for nonstationary signals using the continuous-wavelet transform (CWT). The conventional method of producing a time-frequency map using the short time Fourier transform (STFT) limits time-frequency resolution by a predefined window length. In contrast, the CWT method does not require preselecting a window length and does not have a fixed time-frequency resolution over the time-frequency space. CWT uses dilation and translation of a wavelet to produce a time-scale map. A single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet. Previous workers have converted a time-scale map into a time-frequency map by taking the center frequencies of each scale. We transform the time-scale map by taking the Fourier transform of the inverse CWT to produce a time-frequency map. Thus, a time-scale map is converted into a time-frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented. We refer to such a map as the time-frequency CWT (TFCWT). We validate our approach with a nonstationary synthetic example and compare the results with the STFT and a typical CWT spectrum. Two field examples illustrate that the TFCWT potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization.
A theoretical comparison is made of PP and PS angle–dependent reflection coefficients at the top of two fractured-reservoir models using exact, general, anisotropic reflection coefficients. The two vertical-fracture models are taken to have the same total crack density. The primary issue investigated is determination of the fracture orientation using azimuthal AVO analysis. The first model represents a single-fracture set and the second model has an additional fracture set oblique to the first set at an angle of 60°. As expected, the PP-wave anisotropy is reduced when multiple fracture sets are present, making the determination of orientation more difficult than for the case of a single-fracture set. Long offsets are required for identification of dominant fracture orientations using PP-wave AVO. PS-wave AVO, however, is quite sensitive to fracture orientations, even at short offsets. For multiple-fracture sets, PS signals can potentially be used to determine orientations of the individual sets.
- North America > United States > New Mexico > Permian Basin > Delaware Basin > Upper Pennsylvanian > Vacuum Field > San Andreas Formation > San Andreas Formation > Upper San Andreas Formation (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin > Upper Pennsylvanian > Vacuum Field > San Andreas Formation > Lower San Andreas Formation > Upper San Andreas Formation (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin > Upper Pennsylvanian > Vacuum Field > Lovington Formation > San Andreas Formation > Upper San Andreas Formation (0.99)
- (5 more...)
Synergistic Porosity Mapping In the Upper Cretaceous of the Chiapas Region Using Spectral Decomposition And Neural Network Inversion
Burnett, Michael D. (Fusion Petroleum Technologies, Inc.) | Castagna, John P. (Fusion Petroleum Technologies, Inc.) | Camargo, German (Fusion Petroleum Technologies, Inc.) | Chen, He (Fusion Petroleum Technologies, Inc.) | Sanchez, Julian Juarez (Petroleos Mexicanos) | Santana, Alberto (Petroleos Mexicanos) | Hernandez, Efrain Mendez (Petroleos Mexicanos)
ABSTRACT A reservoir study was conducted at Gaucho field in the Chiapas of Southern Mexico with the primary objective being to determine porosity in the base of the upper Cretaceous carbonate in order to facilitate further field development. Conventional seismic impedance inversion alone did not adequately predict porosity nor did neural network predictions with conventional seismic attributes. Spectral decomposition, seismic impedance inversion, and neural network inversion were integrated to produce an estimated porosity cube at the target level that provided excellent porosity indication in validation wells. The lateral variation of porosities within the area range from about 2% to more than 30%. Thus, the application of these techniques allowed final adjustment of drilling locations in order to capture the maximum local porosity possible. Resulting porosity maps within the field area are shown to have important implications for field development and further exploration in this area. This study defines a relationship between porosity thickness and peak frequency, and between the magnitude of the average effective zone porosity and peak amplitude. Additionally, the study illustrates the importance of training a neural network properly with (1) appropriate input attributes, and (2) utilization of wells which cover the spectrum of possible porosity encountered in the area. We show how such a methodology can be applied to carbonate reservoirs to distinguish locations with minimal to no effective porosity from areas with excellent porosity where additional development drilling can be fruitful.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.51)