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.

Seismic high-resolution processing is crucial to enhance the accuracy and reliability of seismic data, particularly in the exploration and development of complex hydrocarbon reservoirs. Conventional high-resolution processing methods, exemplified by sparse spike deconvolution (SSD), typically rely on the assumption of sparse reflectivity. Recent advancements in deep learning have introduced a deep convolutional neural network (DCNN) into high-resolution processing and relaxed it from rigorous physical assumptions. However, DCNN high-resolution processing often suffers from limited generalization capabilities, primarily due to the scarcity of labeled data and variations in data quality. In this study, we identify a frequency learning bias in DCNN high-resolution processing, in which the network initially prioritizes dominant-frequency components before gradually addressing lower and higher frequencies. This bias results in inadequate learning of high-frequency components. We develop a frequency-dependent multiscale network (FMN) informed by the frequency multiscale transformation theory. The FMN aims to improve the frequency learning direction of neural networks, enabling them to simultaneously and efficiently learn information across low-, dominant-, and high-frequency components. Our synthetic and 3D field data experiments demonstrate that our FMN outperforms SSD and DCNN methods, providing superior generalization and higher fidelity in producing high-resolution results.

As the difficulty of oil and gas field exploration and development increases both domestically and internationally, onshore exploration targets have gradually shifted from the shallow to the deep and from conventional oil and gas reservoirs to unconventional ones. Particularly in the exploration and development of unconventional oil and gas horizontal wells, there is an increasing demand for higher precision and quality of seismic data to better identify formation lithology, rock fractures, and improve the characterization of reservoirs, reservoir positioning, and connectivity. Wide-azimuth seismic exploration possesses significant technical advantages in addressing exploration challenges such as lithologic exploration, small fault imaging, and detailed characterization of oil and gas reservoirs. Wide azimuth seismic data reduces blind spots in seismic acquisition and improves the imaging accuracy of small faults. Notably, there exist distinct anisotropic characteristics in fault areas and fractured reservoirs. Wide azimuth seismic data is particularly advantageous for studying amplitude variation with variations in amplitude with offset (AVO), incident angle (AVA), or azimuth (AVAZ), as well as velocity with azimuth (VVA). These variations aid in identifying faults, fractures, and changes in formation lithology. As the focus of oil and gas exploration gradually shifts to complex lithological reservoirs and unconventional oil and gas reservoirs, narrow azimuth seismic exploration has been gradually replaced by wide azimuth exploration. However, as observation azimuth increases, challenges related to velocity variations with azimuth, azimuth-related traveltime differences, and azimuth-related anisotropy arise. Based on wide-azimuth seismic data from tight gas reservoirs in western China, this study conducted wide-azimuth anisotropic velocity analysis, OVT domain data regularization processing, OVT domain prestack time/depth migration, and horizontally transverse isotropy (HTI) azimuth anisotropy correction techniques. After applying specialized processing to the wide-azimuth seismic data, significant improvements were observed in the S/N and resolution of the target layer. The delineation of fractures related to hydrocarbon sources also became more distinct. These advancements not only provided high-quality results for high-fidelity, high-resolution imaging of tight gas reservoirs but also provided azimuth volume corresponding to fast and slow wave velocities for seismic data interpretation, facilitating velocity variation with azimuth (VVAZ) fracture detection and AVO analysis research.

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.

Estimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring.

We propose an accurate method for modeling acoustic wave propagation. The spatial derivatives are calculated using Fourier transform to reduce spatial numerical dispersion. The standard staggered grid is adopted to suppress the non-causal ringing artifacts as in the traditional pseudo-spectral method. Moreover, to eliminate time dispersion arising from the discretization of the time derivative, an additional time-dispersion elimination term is introduced. As a result, the present method not only retains the advantages of the conventional pseudo-spectral method such as coarser spatial sampling or higher spatial derivative approximation accuracy, but also achieves higher temporal derivative approximation accuracy due to the adoption of the additional time-dispersion elimination term. Numerical examples demonstrate that the temporal dispersion elimination process can be contaminated by spatial numerical dispersion. Thus, the temporal and spatial numerical dispersions should be handled simultaneously, as proposed in this paper, to achieve accurate acoustic simulation.