Hydrocarbon detection via quantum mechanics–based highlight volumes extraction

Abstract

Spectral decomposition, aiding for direct hydrocarbon detection, generally employs time–frequency analysis methods to characterize the time-varying frequency contents of the subsurface layers. However, ongoing efforts to improve time–frequency analysis resolution still face limitations, leading to inaccurate spectral decomposition. In this study, a quantum mechanics–based highlight volumes extraction method, which includes the quantum peak amplitude above average volume and the quantum peak frequency volume, is proposed as a novel spectral decomposition method for hydrocarbon detection. Seismic data are transformed into the time–frequency domain using continuous wavelet transform, and then each sample's amplitude spectrum of each trace is projected on a specific basis constructed by the wave functions using the Schröedinger equation. This yields the corresponding projection coefficient for each sample's amplitude spectrum. For each projection coefficient, the quantum peak amplitude above average volume is calculated by subtracting the average amplitude from the maximum amplitude. The quantum peak frequency volume consists of the local frequency points where the quantum peak amplitude is the maximum. Our approach stands out for its ability to indicate strong amplitude anomalies typically associated with the hydrocarbons and precise gas reservoir locations and has also been validated to handle seismic data with low signal-to-noise ratio well. Model tests and field data applications show the effectiveness and the advantages of the proposed quantum mechanics–based highlight volumes extraction method. The comparison with the conventional methods illustrates that the proposed quantum mechanics–based highlight volumes extraction method has higher temporal and spatial resolution and is more accurate in detecting the hydrocarbons in the gas reservoir. However, it may require longer computational times compared with the conventional methods. This work aims to complement the current spectral decomposition techniques with a new quantum mechanics–based highlight volumes extraction method.