Spectral derived attributes analysis and interpretation using enhanced spectral decomposition method - A Case Study of Penobscot and Stratton Fields

Abstract

Spectral analysis of data, whether seismic or well-log, provides another dimension of analysis and interpretation. These integrations are expected to lead to more accurate and detailed characterization of subsurface formations, which will have important implications for the oil and gas industry and other fields that rely on geophysical data. Spectral decomposition has been used to extract and determine lithological boundaries, reservoirs, etc. However, the methods fail at providing both high time and frequency resolution.  The window length and the fact that the actual signal does not drop to zero outside of the artificially constrained time frame determine the resolution of the technique. Additionally, these techniques typically do not make use of the crucial thickness data that is computed in the Fourier’s phase spectrum. Spectral decomposition is a fundamental mathematical tool that is widely used in machine learning and other fields of data science and engineering and forms the basis of many machine learning algorithms and techniques. We propose to use an improved spectral decomposition technique known as Continuous Amplitude Phase Spectrum (CAPS), which offers high frequency resolution as well as high time/depth resolution of the amplitude and phase spectra. The study used publicly available seismic dataset from two fields, i.e., Stratton and Penobscot, and two wells from the Penobscot field, L-30 and B-41, with gamma-ray, sonic, density, and neutron porosity logs. The areas of interest highlighted by the features derived from CAPS decomposition of the seismic and well log data were found to be in good agreement with the reported findings.