Achieving a proper time-frequency (TF) resolution is the key to extract information from seismic data using TF algorithms and characterize reservoir properties using decomposed frequency components. The generalized S-transform (GST) is one of the most widely used TF algorithms. However, it is difficult to choose an optimized parameter set for the whole seismic data set. In this paper, we propose to set the parameters of the GST adaptively using the instantaneous frequency (IF) of seismic traces. Our workflow begins with building a relationship between the parameter set of the GST and IF using a synthetic wedge model. We use the IF as an indicator for the time thickness of each trace in the wedge model. We then compute the TF spectrum of each trace using the GST with different parameter sets and compare the similarity between the computed TF spectrum and theory TF spectrum. The parameter set with the largest similarity is regarded as the best parameter set for each trace in the wedge model. In this manner, we build a relationship between the parameter set and IF value. We can finally choose the optimum parameter set for the GST according to the IF values of seismic traces. We name the proposed workflow as the self-adaptive GST (SAGST). To demonstrate the validity and effectiveness of the proposed SAGST, we apply it to synthetic seismic traces and field data. Both synthetic and real data examples illustrate that the SAGST can obtain a TF representation with a high TF resolution.

The S transform (ST) is one of the most commonly used time-frequency (TF) analysis algorithms and is commonly used in assisting reservoir characterization and hydrocarbon detection. Unfortunately, the TF spectrum obtained by the ST has a low temporal resolution at low frequencies, which lowers its ability in thin beds and channels detection. In this letter, we propose a three parameters ST (TPST) to optimize the TF resolution flexibly. To demonstrate the validity and effectiveness of the TPST, we first apply it to a synthetic data and a synthetic seismic trace and then to a filed data. Synthetic data examples show that this TPST achieves an optimized TF resolution, compared with the standard ST and modified ST with two parameters. Field data experiments illustrate that the TPST is superior to the ST in highlighting the channel edges. The lateral continuity of the frequency slice produced by the TPST is more continuous than that of the ST.

Lithology classification in well logging plays a significant role in evaluating the quality of oil and gas reservoirs. Conventionally, the manual interpretation method is of much more limited use, partly because it is time-consuming, but mainly because it is subjective. This is due primarily to the massive volume of logging data and the dependence of the experience of geophysical practitioners. By considering the features that logging data are typically sequential and long short-term memory (LSTM) network is well-suited to process a sequential signal, an LSTM-based architecture is proposed to identify rock facies based on borehole data automatically in the study area. The tests based on data from one well of the tight gas sandstone reservoir demonstrate that, when the sample size and the number of hidden layer neurons are appropriately set, the trained LSTM-based Adam optimizer can precisely recognize the rock facies boundaries than that based on Sgdm and Rmsprop optimizers. Additionally, results on another eight wells in the same study area statistically show a good generalization of the trained LSTM. Moreover, another complex reservoir with similar lithology interbedding also demonstrates the usefulness of the LSTM-based network.

In this letter, a new method is introduced for a seismic time-frequency (TF) analysis. The proposed method is called synchrosqueezing generalized S-transform (SSGST), which belongs to a postprocessing procedure of the GST. The frequency-dependent Gaussian window used in the standard S-transform may be not suitable for real applications. In order to overcome this limitation, the frequency-dependent Gaussian window is replaced by a parameterized function containing three parameters. These three parameters result in flexibility in the variation of TF resolution. Then, the synchrosqueezing transform is employed to squeeze the TF coefficients of the GST to achieve an energy-concentrated TF representation. Synthetic examples and field data show that the SSGST achieves a high resolution and has the potential in highlighting geological structures with high precision.

Seismic data reconstruction is one of the essential steps in the seismic data processing. Recently, the deep learning (DL) models have attracted huge attention in seismic exploration, which has been applied to seismic data reconstruction, especially the convolutional neural network (CNN)-based methods. However, the general CNN-based models only consider seismic features in the time domain and do not take into account the frequency features. Moreover, there are detailed features lost due to the downsampling scheme. We propose a wavelet-based residual DL (WRDL) network to reconstruct the incomplete seismic data. By selecting the U-Net as the backbone, we introduce the discrete wavelet transform (DWT) to replace the pooling operations, whose invertibility property benefits reserving the detailed features. Furthermore, the inverse wavelet transform (IWT) with the expansion convolutional layer is introduced to restore the feature maps. In addition, we adopt the residual blocks into the proposed model to promote the training accuracy and avoid the overfitting issue. To accurately and effectively reconstruct the missing seismic data, we propose a hybrid loss function based on the structural similarity (SSIM) loss and the Huber loss. Numerical experiments on synthetic data and field data show that the WRDL model reconstructs the missing seismic data more accurately than the U-Net and MWCNN models, including the irregularly missing seismic data and the consecutively missing seismic data with a big gap. Furthermore, the qualitative and quantitative results demonstrate the advantages of the proposed hybrid loss function over the commonly used traditional loss for seismic data reconstruction.

Seismic fault interpretation is one of the key steps for seismic structure interpretation, which is a time-consuming task and strongly depends on the experience of the interpreter. Aiming to automate fault interpretation, we have considered it as an image segmentation issue and adopt a solution using a residual UNet (ResUNet), which introduces residual units to UNet. Using the ResUNet model, we develop a fault-versus-azimuth analysis based on offset vector tile data, which, as common-azimuth seismic data, provide more detailed and useful information for interpreting seismic faults. To avoid manual efforts for picking training labels and the inaccuracy introduced by different interpreters, we use synthetic seismic data with a random number of faults with different locations and throws as the training and validation data sets. ResUNet is finally trained using only synthetic data and tested on field data. Field data applications show that the proposed fault-detection algorithm using ResUNet can predict seismic faults more accurately than coherence- and UNet-based approaches. Moreover, geologic fault interpretation results computed using common-azimuth data exhibit higher lateral resolution than those computed using poststack seismic data.