Over the last decades, seismic random noise attenuation has been dominated by transform-based denoising methods over the last decades. However, these methods usually need to estimate the noise level and select an optimal transformation in advance, and they may generate some artifacts in the denoising result (e.g., nonsmooth edges and pseudo-Gibbs phenomena). To overcome these disadvantages, we trained a deep convolutional neural network (CNN) with residual learning for seismic data denoising. We used synthetic seismic data for network training rather than seismic images, and we adopted a method to preprocess the seismic data before it was inputted in the network to help network training. We demonstrate the performance of the deep CNN in seismic random noise attenuation based on the synthetic seismic data. Results of numerical experiments show that our network adaptively and effectively suppresses noise of different levels and exhibits a competitive performance in comparison with the traditional transform-based methods.

The identification process of different lithologies, hydrocarbons, and water-saturated zones in oil and gas industries involves petrophysical studies that are carried out by geoscientists using different software packages. This study aims to propose a method by integrating mean cluster analysis and well logs to identify dominant lithologies, pore fluids, and fluids contact. For this purpose, initially, K-mean cluster analysis is applied to density log and P-wave velocity data of three wells in order to group them into different clusters. Based on centroids of each cluster, different lithologies have been identified. The density log equation has been utilized to compute the porosity of each cluster, and the mean of each density log cluster is used as matrix density. Next, sonic log equation has been inverted to compute the fluid velocity and the mean of each P-wave velocity cluster is used as matrix velocity. For the fluid density, sonic and density log equations are jointly inverted to compute the fluid velocity of each cluster. The fluid bulk modulus and acoustic impedance are computed using fluid density and velocity. Based on the results of K-mean cluster analysis, different lithologies (shale, sandstone, and limestone) have been recognized successfully. In well-1, hydrocarbon and water-saturated zones are successfully identified and fluids contact has been established in the zone of interest. However, well-2 and well-3 did not show any indications of the presence of hydrocarbon in the respective zones.

Continuous wavelet transformation (CWT) as a new mathematical tool has provided deep insights for the identification of localized anomalous zone in the time series data set. In this study, a three-layer geological model is investigated by CWT to locate seismic reflections temporally and spatially. This model consists of three layers, where the third layers of the anticline structure are assumed to act as a pure sandstone hydrocarbon reservoir with 10% porosity. The equation of Gassmann has been implemented for the pore fluid substitution in the reservoir. Synthetic seismic data are generated for the three-layer geological model. Due to the presence of noise, it is always difficult to interpret seismic data. But, CWT has the ability of noise reduction, improving the visualization of a data set and locating the anomalies in terms of scalogram and 3D CWT coefficients. Synthetic seismic data of the geological structure are transformed by CWT. The successful transformation of P-wave velocity, synthetic seismic data and acoustic impedance inversion provided evidence to distinguish different interfaces accurately. CWT has successfully located seismic reflections by localizing high-energy spectrum within the cone of influence. Three high-energy spectrums have been identified at 0.8 s, 1 s and 1.07 s, and it exactly matches the seismic data and three-layer geological model.

For full-waveform inversion (FWI), building the salt structure velocity model is a challenging problem. In the absence of a high-fidelity initial model, low-frequency seismic data are indispensable for accurate reconstruction of the long-wavelength components of salt domes, which are often missing in field data. The envelope isolates the amplitude information from the seismic data and has a stronger linear relationship with the velocity than the seismic data. It is used to enhance the convexity of the objective function in an envelope inversion (EI). However, the properties of the envelope are not effectively utilized in EI due to the lack of their proper understanding. In order to solve the problems that EI has in building the salt structure velocity, such as the cycle-skipping problem, the gradient calculation problem caused by the waveform Fréchet derivative, and the multisolution problem caused by the missing polarity information of the envelope, we propose a multiscale direct signed EI (MSDSEI) based on the direct envelope Fréchet derivative and multiscale signed envelope. The development process from EI to MSDSEI also inspired us to understand the physical mechanism involved in the low-frequency components of the envelope: the signed envelope can be regarded as a low-pass filter of the reflection sequences in the subsurface. Thus, we use the smoothness and phase-independent properties of the envelope to propose an envelope-based full-band seismic data reconstruction method for multiscale FWI. Finally, the performance of various EI methods is verified on the Sigsbee2A model.