Detection of Cyclic Patterns Using Wavelets: An Example Study in the Ormskirk Sandstone, Irish Sea |
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| Authors: | Nestor A Rivera S Ray Jerry L Jensen Andrew K Chan and Walter B Ayers |
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| Institution: | (1) Department of Petroleum Engineering, Texas A&M University, College Station, Texas, 77843-3116;(2) Department of Electrical Engineering, Texas A&M University, College Station, Texas, 77843-3128;(3) Department of Geology and Geophysics, Texas A&M University, College Station, Texas, 77843-3115 |
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| Abstract: | This study shows how wavelet analysis can be used on well log and drill core data to identify cyclicity in sedimentary sequences. Three possible methods for determining wavelength were investigated: the Morlet wavelet, the Fourier transform, and the semivariogram. When applied to several hypothetical signals similar to those observed in petrophysical measurements in hydrocarbon reservoirs, all three methods could identify the presence of cyclicity. Only the wavelet scalogram, however, gave a clear indication of when the cyclic element was present and where frequency changes occurred in the signal. To illustrate the wavelet analysis, we processed well log and core data from a well in the Ormskirk Sandstone and determined the wavelet coefficients for each zone and the wavelengths of the strongest cyclicities. The cyclicities observed corresponded well with sedimentary features of the formation (e.g., channels and channel sets). Also, ratios of the cyclicity wavelengths corresponded with ratios of the Milankovitch precession, obliquity, and eccentricity periods. This result is in agreement with other investigators, who have proposed that Milankovitch-driven climate changes exercised an important control on Ormskirk Sandstone deposition. |
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| Keywords: | cyclostratigraphy Morlet Wavelet Fourier transform semivariogram |
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