Tool For Seismic Interpretation Research Articles

Application of second order multi-synchrosqueezing transform for seismic data analysis

Time-frequency methods, which allow us to observe the seismic signal in time and frequency domains simultaneously, can efficiently discern the seismic response features of geological bodies in different frequency bands. In this paper, a newly developed time-frequency method named second order multi-synchrosqueezing transform (SMSST) is introduced to analyze seismic data. SMSST combines the second-order instantaneous frequency estimation and the iterative idea of multi-synchrosqueezing transform to approximate the true instantaneous frequency trajectory. It solves the problems of non-reassignment points and the sensitivity to noise at the same time. Synthetic example is employed to validate the superiority of SMSST in time-frequency analysis. Results obtained from four different methods show that SMSST offer the best performance in terms of energy-concentration and noise robustness. Applications on field data further demonstrate that SMSST-based method is suitable and effective to highlight geological information and stratigraphic features compared to the previous techniques. The capabilities of SMSST for accurate reservoir localization with high resolution, making it a promising and potential tool in seismic interpretation.

Seismic resonance: Wavelet-free reflectivity retrieval via modified cepstral decomposition

Spectral decomposition is a proven tool in seismic interpretation. It helps interpreters highlight channels and map temporal bed thickness and other geologic discontinuities. In spectrally decomposed seismic data, amplitude spectra contain notch patterns. The period of these notch patterns inversely correlates to the reservoir layer’s thickness and/or its interval velocity. Additional cepstral decomposition will directly extract the bed time thickness or arrival time under simple reflectivity setups. Based on these observations, we have adopted a new workflow for reflectivity retrieval in stably phased seismic. There is no need to understand the details of the wavelet in this workflow. We find that the reflector time and its “apparent strength” can be identified in a transformed seismic resonance domain resulting from a modified cepstrum analysis. We find that linear hot spots in this domain lead to direct quantification of the reflectivity series. The timing and strength of the linear hot spots reveals reflector times and scaled reflectivity coefficients. We applied this method for thickness prediction for a target deepwater reservoir with a complex geologic setting. Because of the large reservoir thickness variations and weak impedance contrast with underlying lithology, identification of the base reservoir and therefore the reservoir thickness has been historically challenging in this field. In a blind test, the reservoir thicknesses evaluated from this method are close to the true vertical thickness found in wells.

Nonstationary phase estimation: A tool for seismic interpretation?

Spectral decomposition is a standard tool in seismic interpretation to highlight variations in layer thicknesses (e.g., in sand channels and turbidites). This is done by examining changes in peak frequencies in extracted horizon slices (Partyka et al., 1999). Variations in peak frequencies occur due to constructive interference between reflections from the top and bottom of a layer, with lower peak frequencies indicating thicker layers.

Coherence cube enhancement based on local histogram specification

Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections (time slices or profiles) due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification (LHS) techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification (HS) algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.