The seismic quality factor Q quantifies the anelastic attenuation of seismic waves in the subsurface and can be used in assisting reservoir characterization and as an indicator of hydrocarbons. Usually, the Q-factor is estimated by comparing the spectrum changes of vertical seismic profiles and poststack seismic data. However, seismic processing such as the normal moveout (NMO) stretch would distort the spectrum of the seismic data. Hence, we have estimated Q using prestack time migration gathers. To mitigate the NMO stretch effect, we compensate the NMO stretch of prestack seismic gathers in the time-frequency domain. Similar to the log spectral method, our method obtains the Q by measuring the log spectral ratio (LSR) of seismic events of the top and base of the reservoir at a zero-offset seismic trace. The LSR has a linear relationship with a new parameter M by assuming that the source wavelet is a constant-phase wavelet. The parameters M and LSR vary with the offset value (traveltime). We use the values of M and LSR obtained from nonzero-offset seismic traces to simulate the values of M and LSR at a zero-offset seismic trace. Finally, we obtain Q by applying the classic LSR method to the simulated M and LSR. To demonstrate the validity and effectiveness of our method, we first apply it to noise-free and noisy synthetic data examples and then to real seismic data acquired over the Sichuan Basin, China. The synthetic and real seismic applications demonstrate the effectiveness of our method in highlighting high anelastic-attenuation zones.

Although there are plenty of approaches proposed for addressing seismic volumetric dip estimation, it still suffers from several limitations, for example, the expensive computation cost, the perturbations from sequence stratigraphic anomalies, and the difficulty for handling the complicated geologic structures. Recently, deep learning (DL)-based models have been proposed for seismic dip estimation, which use seismic dips calculated using the traditional methods as the training labels. Apparently, these DL-based models can effectively improve the computational efficiency; however, it still subjects to the limitations of the traditional algorithms. We propose a multichannel deep learning (MCDL) model for implementing seismic volumetric dip estimation, mainly including share module (SM), particular module (PM), and fused module (FM). First, we calculate seismic dips using several traditional methods based on 3-D real seismic data as the training labels, which are used to pretrain SM and PM. Then, we propose a workflow to create synthetic seismic data and ground-truth dip labels, which are used to fine-tune SM/PM and train FM. In this way, we can obtain a DL model by considering both the features of synthetic ground-truth dips and the calculated dips from real data. Moreover, we can effectively enhance the generalization ability of MCDL by pretraining with the estimated dip volumes from real data. To demonstrate its validity and availability, we apply MCDL to synthetic data and two 3-D real seismic volumes. The qualitative and quantitative comparisons illustrate the superiority of the proposed model over the traditional methods.

Seismic attenuation estimation is a significant task for characterizing reservoirs and improving the resolution of seismic data. The logarithmic spectral ratio (LSR) is one of the widely used tools for seismic attenuation estimation. However, how to select a suitable frequency bandwidth for the LSR is a difficult task, especially for field data. Moreover, field data are often contained kinds of noises, which makes seismic attenuation estimation difficult and unstable. We proposed an enhanced LSR (ELSR) workflow to estimate seismic attenuation. First, we built a sparse S-transform (SST) to obtain a sparse time-frequency (TF) spectrum of the analyzed seismic trace. Then, based on the SST spectrum, we introduced an ELSR workflow to estimate seismic attenuation. It should be noticed that we provided an adaptive frequency band selection for the LSR. To demonstrate the effectiveness of the proposed workflow, we apply it on both synthetic and field data. Compared with the results from several traditional attenuation estimation methods, the proposed ELSR provides a more stable and more accurate attenuation estimation result and offers the potentials in improving the resolution of post-stacked seismic data.

Fluid movement and grain boundary friction are the two main factors affecting the anelastic attenuation of seismic data. The quality factor Q quantifies the degree of anelastic attenuation and is c...

The existence of coal seams suppresses other useful information, especially the below-thin layers, and is unfavorable for delineating the target reservoirs beneath them. The matching pursuit (MP) based methods are commonly used for removing strong reflections caused by coal seams. They first decompose a seismic trace into several wavelets based on a user-defined wavelet dictionary and then separate the most similar wavelet with the coal seam. However, how to define a complete wavelet dictionary and how to maintain horizontal continuity are two unsolved issues. We propose a multi-scale attention deep learning (MSADL) model for separating and removing seismic strong reflections. First, we suggest a workflow to generate a synthetic data set for model training based on the characteristics of field data and well logs. Next, we build an MSADL model by integrating the discrete wavelet transform (DWT) and convolutional block attention module (CBAM) into the widely used Unet. After model training, we apply the well-trained MSADL model to 3D field data in the Sichuan Basin, China for the separation and removal of strong reflections and characterization of the beneath target thin layers.