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Collaborating Authors
Castagna, John
ABSTRACT As seismic data responds primarily to the elastic properties of the earth, a way to assess the best case ability of seismic data to discriminate lithologies, is to test the procedure on compressional and shear-wave impedances derived from acoustic logs. A methodology for lithological discrimination using Genetic Programming (GP) and elastic attributes derived from well logs is found to classify lithology in Upper Albian deep water tight gas sands reservoirs of the Santos Basin, Brazil, more accurately than Naive Bayesian Classification (NB), Linear Discriminate Analysis (DA) and Multiple Layer Perceptron (RN) classification. Three different GP methodologies, Genetic Programming Classifier Expression (GPCE), Genetic Programming with Multiple Output (GPMO), and Genetic Programming using Gaussian Distribution (GPGD) are found to require fewer calibration data to achieve a better success rate on a blind test dataset than the NB, DA, or RN methods. Three experimentes were run in order to evaluate these methodologies: in situ conditions, in situ plus extension to a 100% water saturation condition, in situ plus extension to a 100% water and 100% gas saturation conditions. GP exhibits over 70% success rate when applied to lithology classification, being the most accurate and robust, when compared to the other methods: GPGD achieved an accuracy of 72.00% for the first experiment, 71.50% for the second and 72.83% for the third, followed by GPMO (69.33%, 75.33%, 72.00%), GPCE (67.83%, 72.50%, 68.00%), RN (58.67%, 69.50%, 67.83), DA (56.83%, 64.67%, 62.67%), and NB (55.50%, 66.17%, 64.67%). Presentation Date: Monday, October 17, 2016 Start Time: 1:50:00 PM Location: 168 Presentation Type: ORAL
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.70)
- Geology > Geological Subdiscipline > Geomechanics (0.48)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
ABSTRACT Phase decomposition is applied to low-impedance hydrocarbon-bearing sands in a clastic section where sand thicknesses vary from the vicinity of tuning to well below tuning. In order to properly interpret seismic phase changes caused by the introduction of hydrocarbons, it is useful to artificially "thin" the targets by high-cut filtering the data, thereby increasing the tuning thickness and making more layers seismically thin. Once the seismic thinning is performed, the amplitudes separate into the expected phase components, resulting in a different spatial distribution of mapped amplitudes than on the original seismic data. A useful method to determine what frequencies are required to obtain proper phase separation in a section with stacked interfering sands, is to apply spectral decomposition to a synthetic seismogram, followed by phase decomposition. Presentation Date: Wednesday, October 19, 2016 Start Time: 3:10:00 PM Location: 140 Presentation Type: ORAL
Abstract Any seismic trace can be decomposed into a 2D function of amplitude versus time and phase. We call this process phase decomposition, and the amplitude variation with time for a specific seismic phase is referred to as a phase component. For seismically thin layers, phase components are particularly useful in simplifying seismic interpretation. Subtle lateral impedance variations occurring within thin layers can be greatly amplified in their seismic expression when specific phase components are isolated. For example, the phase component corresponding to the phase of the seismic wavelet could indicate isolated interfaces or any other time symmetrical variation of reflection coefficients. Assuming a zero-phase wavelet, flat spots and unresolved water contacts may show directly on the zero-phase component. Similarly, thin beds and impedance ramps will show up on components that are 90ยฐ out of phase with the wavelet. In the case of bright spots caused by hydrocarbons in thin reservoirs because these occur when the reservoir is of an anomalously low impedance, it is safe to assume that the brightening caused by hydrocarbons occurs on the component out of phase with the wavelet. Amplitudes of other phase components associated with bright reflection events, resulting perhaps from differing impedances above and below the reservoir, thus obscure the hydrocarbon signal. Assuming a zero-phase wavelet, bright-spot interpretation is thus greatly simplified on the phase component. Amplitude maps for the Teal South Field reveal that the lateral distribution of amplitudes is greatly different for the original seismic data and the phase component, exhibiting very different prospectivity and apparent areal distribution of reservoirs. As the impedance changes laterally, the interference pattern for composite seismic events also changes. Thus, waveform peaks, troughs, and zero crossings, may not be reliable indicators of formation top locations. As the waveform phase changes laterally due to lateral rock properties variations, the position of a formation top on the waveform also changes. By picking horizons on distinct phase components, this ambiguity is reduced, and more consistent horizon picking is enabled.
- North America > United States > Texas (0.29)
- Europe > United Kingdom > North Sea > Central North Sea (0.25)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Skagerrak Formation (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Heather Formation (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Fulmar Formation (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > West Central Graben > Block 21/25 > Anasuria Cluster > Teal South Field > Forties Formation (0.99)
ABSTRACT The frequency-dependent width of the Gaussian window function used in the S-transform may not be ideal for all applications. In particular, in seismic reflection prospecting, the temporal resolution of the resulting S-transform time-frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes. A simple parameterization of the generalized S-transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the S-transform, at the necessary expense of reduced frequency resolution. This is accomplished by replacing the frequency variable in the Gaussian window with a linear function containing two coefficients that control resolution variation with frequency. The linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies. The resulting transform conserves energy and is readily invertible by an inverse Fourier transform. This modification of the S-transform, when applied to synthetic and real seismic data, exhibits improved temporal resolution relative to the S-transform and improved resolution control as compared with other generalized S-transform window functions.
Abstract Spectral-decomposition data are sorted and visualized in a variety of ways. Useful sorting schemes include (1) pseudospectral volumes such as โtuning cubes,โ (2) time-frequency gathers, (3) space-frequency gathers, and (4) common-frequency volumes. Sorting spectral data into maps and cross sections has useful application in visualizing a combination of time, frequency, and offset dimensions.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
Once this is achieved, the following workflow (Figure 1, Modified from Barbato, 2013) is A composite fault detection attribute is produced by applied (Barbato, 2013, Qi and Castagna, 2013): Adaptive Principal Component Analysis of attributes derived from spectral decomposition. The composite fault detection attribute looks similar in time slice view to conventional attributes such as coherency and curvature but is far more readily interpretable in vertical cross-section view. Detailed interpretation of time slices reveals that window based attributes such as coherence can exhibit discontinuities in the incorrect spatial position if the time slice does not correspond to the strongest event in the window. This problem is less severe on time slices of the composite fault detection attribute and faults are thus, more correctly located. These ideas are demonstrated in a case study in the Hitts Lake Field, where faults are verified by missing section in well logs.
- Geology > Structural Geology > Fault (0.52)
- Geology > Structural Geology > Tectonics (0.51)
- North America > United States > Texas > Sabine Uplift > Paluxy Formation (0.99)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > Texas > East Texas Salt Basin > Hitts Lake Field (0.99)
- North America > United States > Louisiana > East Texas Salt Basin (0.99)
Summary Spectral decomposition data are sorted and visualized in a variety of ways. Useful sorting schemes include: (1) pseudo-spectral volumes or "tuning cubes", (2) timefrequency gathers, (3) space-frequency gathers, and (4) common frequency volumes. Sorting spectral data into maps and cross sections has useful application in visualizing a combination of time, frequency and offset dimensions Introduction Spectral decomposition data are multivalued at a given time and spatial position. Interpretation of subtle stratigraphic features from spectra may, thus, involve multidimensional visualization challenges. Consequently, the spectral data are sorted and visualized in a variety of ways (e.g. Partyka et. al., 1999). Useful sorting schemes include: (1) pseudo spectral volumes, such as "tuning cubes", which are essentially frequency dependent amplitude maps that can be loaded into common interpretation platforms for visualization (2) timefrequency gathers, which show frequency spectra as function of time for a single seismic trace, (3) space-frequency gathers, which show lateral variation of spectra for a given time window or along a horizon, and (4) common frequency volumes.
- North America > United States > Texas > West Gulf Coast Tertiary Basin > High Island Field (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Stratton Field > Frio Formation (0.99)
- North America > United States > California (0.37)
- North America > United States > Texas > Harris County > Houston (0.20)
- Geology > Geological Subdiscipline (0.74)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.37)
Summary We compare the spectra of Short Time Window Fourier Transform (STFT) and Constrained Least Squares Spectral Analysis (CLSSA) for spectral minima periodicity. Using time thickness equals 1/df, where df is frequency period, we show that spectral minima approach using CLSSA gives more accurate time thicknesses than STFT for the same analysis window. Starting with a broad band synthetic seismic data generated from a wedge model, we extract selected traces at known temporal thicknesses for time frequency analysis. We cross plot apparent time thicknesses derived from CLSSA and STFT line spectra using the approach above, against true time thicknesses of the wedge model. The result shows apparent CLSSA thicknesses that are strongly correlated with true time thicknesses We extend this study to real seismic data from Hitts Lake Field, onshore Texas and show that the results are consistent with the results from model studies.
Summary Principal Component Analysis (PCA) has been found to be an effective way of combining a variety of fault detection attributes exhibiting discontinuities that line up along particular 3D orientations. The Barnes (2006) discontinuity-filter method can be used to further enhance these results. Fault detection is improved using phase discontinuities obtained using Constrained Least Squares Spectral Analysis (CLSSA; Puryear et al., 2012) which is a higher resolution spectral analysis method than the short time Fourier transform (STFT). As CLSSA has reduced spectral smoothing relative to the STFT it potentially can produce a higher resolution discontinuity attribute. Based on this feature, CLSSA is applied in the Barnett Shale to detect faults. As compared to coherence and curvature, the resulting PCA fault-attribute better resolves minor tectonic and karsting-related faults.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.66)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play > Shale Gas Play (0.66)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.91)