The exploration of deep and subtle oil and gas reservoirs is currently an important means of increasing production in older oil fields. How to effectively identify weak signals with noise is a common problem faced in such reservoirs. Especially for deep seismic reflection data, the application of traditional denoising methods is limited due to the weak energy and small difference frequency band between the effective signals and the noise. A novel method of noise attenuation for weak seismic signals based on compressed sensing and CEEMD (complementary ensemble empirical mode decomposition) was proposed in this work. This method consists of three steps: First, the CEEMD algorithm, a time-frequency analysis method, was introduced into the classic CS (compressed sensing) denoising method to overcome the nonadaptability of CS. CEEMD decomposed the raw seismic signals into sets of IMFs (finite intrinsic mode functions). The IMFs with noise were reconstructed and denoised by CS. In the second step, an enhancement operator was introduced into the penalty term to ensure that the effective signals could be extracted. Finally, the OMP algorithm is adopted to reconstruct the seismic weak signal to prevent the iterative threshold method from damaging weak effective signals. It was demonstrated through the synthetic seismic record and the field seismic data that (a) the CS method can identify the weak signals submerged in the noise by selecting the basic function that is most similar to the effective signals, but it cannot adaptively suppress the high frequency and high wavenumber noise of the complex seismic records; (b) the proposed method overcome the non-adaptability of CS and enhance the edge information described by the curved wave rather than the noises, as a result, the high frequency noise is suppressed while the middle/low frequency noise and weak effective signals are also effectively separated.

In seismic exploration, matching pursuit (MP), which decomposes wavelets based on best signal matching, can not only extract accurate frequency information, but also can pick up the strong amplitudes of possible bright spots adaptively according to the threshold or iteration control. RGB (Red-Green-Blue) blending technique of spectral bands can make full use of the information of all frequency bands, which can reflect the general frequency changes of seismic data reducing the interpretation ambiguity. Therefore, in order to improve the precision of interpretation based on bright spot technique, the authors take both amplitude and frequency into account and propose a new approach for hydrocarbon identification by combining both methods. First, we choose the decomposed wavelets of strong amplitudes related to hydrocarbons in MP algorithm to predict the possible bright spots, so as to remove the effect of unrelated reflections in the bright spots interpretation. Then, RGB blending technique is used to the low-, mid-, and high-frequency spectral bands of the bright spot prediction section to assist for the hydrocarbon identification. Finally, the validity of the method is verified by both the model and field data tests. Results demonstrate that the proposed method can improve the precision of hydrocarbon identification, and reduce the interpretation ambiguity of the reservoirs.

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.