Hydrocarbon detection based on empirical mode decomposition, teager-kaiser energy, and the cepstrum

Seismic waves produce anomalies when they pass through hydrocarbons; these anomalies, which are commonly used to detect hydrocarbons, are manifested differently in different domains. Here, we propose a novel hydrocarbon detection method that combines Empirical Mode Decomposition (EMD), the Teager-Kaiser energy operator (TKEO), and the cepstrum. This method utilizes EMD’s ability to adaptively decompose signals, benefits from the TKEO’s superior performance regarding the focusing of instantaneous energy, and uses the sensitivity of cepstrum domain parameters to hydrocarbons. Here, applying the developed EMD-TKE-Cepstrum method to the Marmousi2 example revealed that it could describe the position and extent of hydrocarbons more clearly than the synchronous compression wavelet transform (SCWT) method. Applying the EMD-TKE-Cepstrum algorithm to field data further confirmed its potential regarding the identification of anomalies associated with hydrocarbon reservoirs.