Seismic processing and interpretation techniques provide important tools for oil and gas exploration in the Songliao Basin in eastern China, which is dominated by terrestrial facies. In the Songliao Basin, a large number of thin-sand reservoirs are widely distributed and they are the primary targets of potential oil and gas exploration and exploitation. An important part of exploration in the Songliao Basin is to accurately describe the distribution of these thin-sand belts and the sand-body shapes. However, the thickness of these thin-sand reservoirs is generally below the resolution of conventional seismic processing. Most reservoirs are thin interbeds of sand and mudstones with strong vertical and lateral variations. This makes it difficult to accurately predict the vertical and horizontal distribution of the thin-sand bodies using conventional seismic processing and interpretation methods. In addition, these lithologic traps are difficult to identify due to the complex controlling factors and distribution characteristics and the strong concealment. These challenges motivate us to improve the seismic data quality to help delineate thin-sand reservoirs. We have used the broadband, wide-azimuth, and high-density integrated seismic exploration technique to help delineate thin reservoirs. We first use field single-point excitation and single-point receiver acquisition to obtain seismic data with wide frequency bands, wide azimuth angles, and high folds, which contain rich geologic information. Next, we perform near-surface Q compensation, viscoelastic prestack time migration, seismic attributes, and seismic waveform indication inversion on the newly acquired seismic data. The 3D case study indicates the benefits of improving the imaging of thin-sand body and the accuracy of inversion and reservoir characterization using our method.

The quality of a seismic section is typically determined by the effectiveness of the frequency band: low-frequency information is advantageous for improving the quality of deep imaging, while high-frequency information helps to enhance resolution. Compressive sensing (CS) has the potential for precise spread extension. However, during the spread spectrum extension, a low signal-to-noise ratio (SNR) may lead to frequency anomalies, compensation artifacts, and issues caused by noise. In this paper, based on the theory of compressive sensing, we derive a formula for full-frequency compensation with a Shearlet denoising constraint. We analyze the effectiveness and feasibility of this method. Compared to other mathematical transforms, Shearlet exhibits superior denoising capabilities and effective signal protection. Additionally, by replacing the identity matrix in the denoising constraint with a sampling matrix, it can accurately reconstruct missing data, even when dealing with discontinuous data. To improve the accuracy and effectiveness of compensation, we perform autocorrelation calculations on the data to extend the wavelet for frequency compensation. We apply the proposed method to various field data, including thin layers, low signal-to-noise ratio regions, missing data, and gas-bearing areas. Examples demonstrate the effectiveness and applicability of the proposed method.