Seismic Resolution Enhancement Research Articles

SeisResoDiff: Seismic resolution enhancement based on a diffusion model

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model — denoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective’s derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.

New technologies for seismic resolution enhancement and bandwidth expansion: Applications in SE Asian Basin

Over the last few years, the petroleum giant and its partner have effectively implemented new marine seismic technologies in this part of the world. During the acquisition phase, the industry has observed the introduction of novel broadband acquisition techniques such as dual-sensor cable, multiple towed streamers (over/under), slanted and variable depth streamers, and isometric areal recording. The application of these techniques has resulted in a noteworthy enhancement of the recorded data’s bandwidth. The focus of this paper is on the improvement of seismic bandwidth expansion resulting from advancements in marine broadband acquisition, innovative processing techniques, and inversion methodology. The outcome leads to a noteworthy enhancement in the vertical resolution, which proves to be valuable in the identification of thin stacked pay beds and the creation of a reservoir model that is constrained by seismic data. This paper focuses on the imaging aspect, particularly the complex structures. The utilization of wide and full azimuth (Circular) recording and the resolution of Gas wipeout issues through seabed acquisition with Ocean Bottom Cables (OBC) have been advantageous. By utilizing de-ghosted or far-field signature modeled techniques on the processing side, it is possible to increase the bandwidth for both legacy and modern high-quality data, such as Q-marine. A novel de-convolution technique has been developed, which can enhance signal frequency to a considerable extent while minimizing or eliminating any impact on noise. The article presents various wavelet transform techniques that can effectively enhance the signal-to-noise ratio (S/N). The methods are noteworthy due to their ability to be swiftly executed post-stack while incurring minimal expenses. Further, the inversion techniques, with a focus on stochastic elastic inversion, generates broadband data, enabling the identification of pay sand that falls below the quarter of wavelength criteria. The developed techniques are novel and have been validated using authentic Offshore Malaysia data.

Seismic resolution enhancement with variational modal-based fast-matching pursuit decomposition

Enhancing vertical resolution and signal-to-noise ratio (S/N) is a key objective in seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and S/N. Traditional fast-matching pursuit (FMP) algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved FMP algorithm that combines the variational-mode decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multiscale decomposition of nonstationary seismic data. Based on the intrinsic mode functions of different scales, the FMP algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that our method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results indicate that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multiscale seismic data.

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

High-resolution seismic processing involves the recovery of high-frequency components from seismic data with lower resolution. Traditional methods typically impose prior knowledge or predefined subsurface structures when modeling seismic high-resolution processes, and they are usually model-driven. Nowadays, there has been a growing utilization of deep learning techniques to enhance seismic resolution. These approaches involve feature learning from extensive training datasets through multi-layered neural networks and are fundamentally data-driven. However, the reliance on labeled data has consistently posed a primary challenge for deploying these methods in practical applications. To address this issue, a novel approach for seismic high-resolution reconstruction is introduced, employing a Cycle Generative Adversarial Neural Network (CycleGAN) trained on authentic pseudo-well data. The application of the CycleGAN involves creating dual mappings connecting low-resolution and high-resolution data. This enables the model to comprehend both the forward and inverse processes, ensuring the stability of the inverse process, particularly in the context of high-resolution reconstruction. More importantly, statistical distributions are extracted from well logs and used to randomly generate extensive sets of low-resolution and high-resolution training pairs. This training set captures the structural characteristics of the actual subsurface and leads to significant improvement of the proposed method. The results from experiments conducted on both synthetic and field examples validate the effectiveness of the proposed approach in significantly enhancing seismic resolution and achieving superior recovery of thin layers when compared with the conventional method and the deep-learning-based method.

Fast sparse Bayesian learning-based seismic resolution enhancement

Seismic high resolution processing plays a crucial role in seismic interpretation and reservoir characterization. However, the low and high frequency of seismic signal are lost owing to the effects of earth filtering and diffraction. The missing of low-frequency increases the energy of wavelet side lobe, which not only enhances the interference with adjacent events, but also lead to side lobe artifacts. High frequency attenuation is more serious, which significantly reduces the seismic resolution. In addition, the current resolution enhancement methods are difficult to achieve desirable result in noise contaminated environment. A novel resolution enhancement method that extends high and low frequencies simultaneously and improves the signal-to-noise ratio (SNR) under the framework of sparse decomposition theory is proposed. Firstly, an over-complete dictionary is constructed using non-zero phase Ricker wavelet and then the effective seismic signal is extracted by sparse decomposition using the fast sparse Bayesian learning (FSBL) algorithm. Secondly, the effective frequency range of the seismic signal is determined through a series of atoms obtained by sparse decomposition. Then, a desired broadband spectrum can be identified depending on this range. By approximating the broadband spectrum of seismic data, the weak high-frequency information can be extended. Furthermore, a correction term is introduced in frequency domain to suppress the side lobes of atoms. The side lobes are well suppressed without distorting the main lobe, meanwhile, both the low and high frequencies are extended. Finally, the validity of this method is verified by synthetic and field data. The results show that this method has better performance than the ordinary deconvolution and spectral whitening methods in processing high resolution data.

Inverse Q-Filtering as a Tool for Seismic Resolution Enhancement: A Case Study from the Carpathian Foredeep

In this study, we analyze the quality factor- Q derived from the Vertical Seismic Profiling (VSP) and sonic log data that was performed in the DS-2 borehole located in the Carpathian Foredeep. We use the obtained quality factor distributions in the inverse Q-filtering (IQF) procedure during active seismic data processing. Another very important and, unfortunately, subjective in nature parameter is the gain function applied to seismic data while performing the IQF. To calculate Q from VSP data we use an iterative approach to the spectral ratio method where we use several shallowest receivers as reference. We observe high variability of the obtained Q values, depending on the reference receiver, however, depth distribution of Q was consistent regardless To increase reliability of the obtained values, an additional method of Q measurements is incorporated, i.e., acoustic travel time dispersion method. We show that incorporating inverse Q-filtering to active seismic data processing flow allows to enhance the vertical resolution of 2D seismic data.

Estimation of seismic attenuation for reservoirs mapping and inverse Q‐filtering: An application on land seismic data

AbstractSeismic attenuation values based on inverse Q‐filtering are useful in enhancing seismic data resolution for quantitative interpretation. However, as field attenuation estimations are often contaminated by noise and overburden effects, an inverse Q‐filter may reduce the signal‐to‐noise ratio. To help reservoir mapping with seismic resolution enhancement, we use well logs and surface seismic data to estimate the attenuation of a depth interval composed of several carbonate oil reservoirs onshore the Arabian Peninsula. From applying a prestack Q inversion on τ–p gathers from a large 3D seismic survey, the estimated effective attenuation values show spatially coherent patterns, likely corresponding to changes in the petrophysical and fluid properties. The patterns of the estimated effective attenuation values correlate considerably with the anticline geometry, with higher values on its crest than its flanks. The small percentage of the negative effective attenuation values indicates that the apparent attenuation and overburden effects are insignificant. At well locations, the effective attenuation values show a good correlation with the average porosity of the main reservoir, verifying that they are related to changes in petrophysical properties in the analysis interval. We propose an extended inverse Q‐filter with two additional parameters to make it more robust and flexible and test it with synthetic and field datasets. We estimate a multiple‐scattering attenuation value from well logs and use it in the proposed filter for the field dataset. The compensation for the multiple‐scattering effects suppresses the overprints of the data components unrelated to the target interval, hence benefitting the quantitative data interpretation.

Improving the Generalization of Deep Neural Networks in Seismic Resolution Enhancement

Seismic resolution enhancement is a key step for subsurface structure characterization. Although many have proposed the use of deep learning (DL) for resolution enhancement, these are typically hindered by the limitations in the application of synthetically trained networks onto real datasets. Domain adaptation (DA) offers an approach to reduce this disparity between training and inference data, aiming through the application of data transformations to bring the distributions of both data closer to each other. We propose a simple DA procedure, termed MLReal-Lite (the light version of the earlier introduced MLReal), that mainly relies on linear operations, namely convolution and correlation; these transformations introduce aspects of the field data into the synthetic data prior to training, and vice-versa with regard to the inference stage. Taking 1-D and 2-D resolution enhancement tasks as examples, we show how the inclusion of MLReal-Lite improves the performance of neural networks. Not only do the results demonstrate notable improvements in seismic resolution, they also exhibit a higher signal-to-noise ratio (SNR) and better continuity of events, in comparison to the tests without MLReal-Lite. Finally, while illustrated on a resolution enhancement task, our proposed methodology is applicable for any seismic data of dimensions N-D, offering a DA applicable from well ties through to 3-D seismic volumes, and beyond.

Seismic Resolution Enhancement by Spectral Shaping Using Shaping-Regularized Inversion

Seismic deconvolution aims to improve the vertical seismic resolution by compressing the seismic wavelet and extending the seismic frequency bandwidth. Deconvolution in the frequency domain is normally implemented in three steps: wavelet estimation, deconvolution operator construction, and data filtering. We propose a seismic resolution enhancement approach by spectral shaping using shaping-regularized inversion. Instead of designing a deconvolution operator based on the extracted wavelet, we formulate an inverse problem using the seismic spectrum as the diagonal kernel matrix and a predefined filter as the expected spectrum. Assuming the randomness in the reflectivity and smoothness of the spectral shaping operator, we estimate the spectral shaping operator by inversion via a shaping regularization scheme, which imposes constraints by shaping the estimated spectral shaping operator model to the admissible model space. The role of the shaping regularization in inversion is to ensure the continuity and smoothness of the spectral shaping operator. We use a synthetic model and real land seismic data to demonstrate the effectiveness of the proposed approach in seismic resolution enhancement.

Q Estimation via the Discriminant Method Based on Error Modeling

Seismic attenuation is one of the crucial attributes for seismic resolution enhancement and reservoir characterization, which is often described by quality factor Q. Among the commonly used methods for Q estimation, the frequency based method is demonstrated for effectively estimating Q factor, mainly including the frequency shift (FS) based, and the spectral correlation (SC), and the logarithm spectral ratio (LSR) based methods. However, the frequency spectrum of the received wave may be affected by the closely adjacent reflections, which may result in an unreliable Q estimation. To handle this limitation, the error modeling based discriminative approach in machine learning is proposed in this study. We utilize the seismic wave first received to construct the classifier based on the reproducing kernel Hilbert spaces representer theorem, then give an effect Q estimation by applying the classifier to the wave second received. Furthermore, to improve the robustness of the estimation results, the error models of the classifier are introduced. The loss functions are proposed to correspond to the distributions of the error models, from which two different implementations of Q estimation are derived. When applying to the synthetic data, the corresponding results show that the proposed method greatly improves the robustness and accuracy of the conventional methods. Field data test is also provided and demonstrates the effectiveness of our proposed method.

Deep learning for multitrace sparse-spike deconvolution

Sparse-spike deconvolution (SSD) is an important method for seismic resolution enhancement. With the wavelet given, many trace-by-trace SSD methods have been proposed for extracting an estimate of the reflection-coefficient series from stacked traces. The main drawbacks of trace-by-trace methods are that they neither use the information from the adjacent seismograms nor do they take full advantage of the inherent spatial continuity of the seismic data. Although several multitrace methods have been consequently proposed, these methods generally rely on different assumptions and theories and require different parameter settings for different data applications. Therefore, traditional methods demand intensive human-computer interaction. This requirement undoubtedly does not fit the current dominant trend of intelligent seismic exploration. Therefore, we have developed a deep learning (DL)-based multitrace SSD approach. The approach transforms the input 2D/3D seismic data into the corresponding SSD result by training end-to-end encoder-decoder-style 2D/3D convolutional neural networks (CNN). Our key motivations are that DL is effective for mining complicated relations from data, the 2D/3D CNN can take multitrace information into account naturally, the additional information contributes to the SSD result with better spatial continuity, and parameter tuning is not necessary for CNN predictions. We determine the significance of the learning rate for the training process’s convergence. Benchmarking tests on the field 2D/3D seismic data confirm that the approach yields accurate high-resolution results that are mostly in agreement with the well logs, the DL-based multitrace SSD results generated by the 2D/3D CNNs are better than the trace-by-trace SSD results, and the 3D CNN outperforms the 2D CNN for 3D data application.

Seismic resolution enhancement by the Curvelet transform

Zone A in Nanpu Sag of Bohai Bay has the characteristics of multiple provenances, multiple sedimentary systems, fast phase transition, many faults, a short fault distance, thin reservoirs and rapid lateral changes. At present, due to the low resolution of seismic data and thin sand layer of the target formation, it is difficult to depict the channel sand body, and it is also difficult to meet the requirements of accurately implementing low amplitude structures, reservoir prediction and well location deployment. When a seismic wave propagates in the stratum, it has the characteristics of multi-scale, multi-resolution and multi-directionality, while the Curvelet transform has the advantage of expressing these characteristics. In this paper, according to the geological characteristics and seismic response characteristics of continental thin interbedded strata in zone A, the seismic data are first transformed to the Curvelet domain. Then, the formation absorption and seismic wave attenuation are compensated in the Curvelet domain. Lastly, the data mentioned above are inversely transformed into the time–space domain. This paper presents the method and process of using the Curvelet transform to enhance the resolution of seismic data in thin interbedded strata. The application of a theoretical model and field data show that this method can effectively enhance the resolution of seismic data while maintaining a high signal-to-noise ratio. The innovative use of this method in zone A lays the foundation for the follow-up work of this structure.

An Adaptive Noise-Free Method of Seismic Resolution Enhancement Based on Extrapolated Multiresolution Singular Value Decomposition

The maximum vertical seismic resolution is a critical parameter in seismic data processing and interpretation. In general, conventional methods such as spectral whitening and zero-phase deconvolution assume randomness in the reflectivity spectrum that may not always be valid. The method proposed herein, extrapolated multiresolution singular value decomposition (EMRSVD), avoids some of these assumptions. We reasonably assume that original seismic data constitute a low-frequency component of high-resolution data, of which high-frequency component is attenuated. To recover this high-resolution component, EMRSVD adaptively extrapolates attenuated high-frequency information based on multiresolution singular value decomposition (MRSVD) and polynomial fitting to broaden the valid frequency bandwidth and improve the resolution of original seismic data. In our work, we first decompose a signal into a series of approximate and detailed subsignals exhibiting different resolutions using MRSVD. Then, we apply a polynomial fitting algorithm to the singular values corresponding to the detailed subsignals and obtain an expression of the singular values. Next, based on this expression, we extrapolate new singular values to construct new detailed subsignals. We repeat this process iteratively, where the number of iterations is determined by a modified variance model with an exponential transformation. Finally, we add these extrapolated detailed subsignals back into the original signal to obtain a high-resolution result. Examples with two synthetic signals and one field seismic data demonstrate that the proposed method significantly improves the resolution of seismic data without introducing noise and efficiently broadens the frequency bandwidth of reflection signals without sacrificing low-frequency information, which facilitates the resolution of thin layers.

Recent Developments in Local Wave Decomposition Methods for Understanding Seismic Data: Application to Seismic Interpretation

This article provides a general overview of local wave decomposition (LWD) methods including empirical mode decomposition (EMD), ensemble EMD, complete ensemble EMD with adaptive noise, synchrosqueezed transform and variational mode decomposition applied to seismic interpretation, covering the theoretical background and applications. We explain how LWD methods can be reviewed as a special and stronger case of the classical seismic time–frequency methods and place emphasis on the interest of using such LWD methods in seismic instantaneous frequency extraction, seismic attenuation estimation and seismic resolution enhancement.

Seismic resolution enhancement using scale transform in the time-frequency domain

Reducing uncertainty in geologic interpretation of petroleum reservoir containing thin layers requires increasing vertical resolution via appropriate advanced resolution enhancement methods. This problem was resolved here by introducing an alternative approach in resolution enhancement. Our method uses Gabor deconvolution (GD) combined with wavelet scaling. First, the seismic trace is transformed in the time-frequency domain using the Gabor transform. Subsequently, the Gabor magnitude spectrum of the seismic trace is smoothed to estimate the wavelet magnitude, which is then divided by the original value on the Gabor magnitude spectrum along the frequency axis to design a scale transformation filter. Finally, the filtered Gabor magnitude spectrum of the seismic trace is transformed back to the time domain using an inverse Gabor transformation. The result of using this technique is an increase of the dominant frequency, which produces a higher resolution of the seismic trace compared with using only the original GD. This method was applied to two synthetic and one field seismic data sets and compared with using only a GD. After applying the new approach, all three data sets indicate an extension in bandwidth (BW) and an enhancement in the resolution adequate for thin-layer seismic interpretation. When compared with the data sets using only a GD, the new approach produced comparable extension in the effective BW, while it pushing the dominant frequency to higher values. This allowed the imaging of many thin layers and geologic intervals in the field data example that could not be interpreted by the GD method.

Seismic resolution enhancement in shale-oil reservoirs

We developed a case study of seismic resolution enhancement for shale-oil reservoirs in the Q Depression, China, featured by rhythmic bedding. We proposed an innovative method for resolution enhancement, called the full-band extension method. We implemented this method in three consecutive steps: wavelet extraction, filter construction, and data filtering. First, we extracted a constant-phase wavelet from the entire seismic data set. Then, we constructed the full-band extension filter in the frequency domain using the least-squares inversion method. Finally, we applied the band extension filter to the entire seismic data set. We determined that this full-band extension method, with a stretched frequency band from 7–70 to 2–90 Hz, may significantly enhance 3D seismic resolution and distinguish reflection events of rhythmite groups in shale-oil reservoirs.

Seismic Resolution Enhancement by Frequency-Dependent Wavelet Scaling

When seismic waves propagate through the earth, their high-frequency energy is absorbed by subsurface viscoelastic media. Seismic wavelet appears to be stretched out, as it is dominated by low-frequency components. In order to enhance seismic resolution, we propose here a wavelet compression method that utilizes the scale characteristic in the Fourier transform. The novelty of the scheme is a frequency-dependent scaling that extends the amplitude spectrum to both high- and low-frequency axes simultaneously. This is for the first time to make this frequency-dependent proposal, instead of a constant scaling scheme in the classic Fourier theory. It compresses seismic wavelet in the time domain and also simplifies the wavelet form effectively. This frequency-dependent scaling scheme leads to a transferring filter that is applicable to seismic field data. It results in an improvement in data resolution and in the ability of thin-layer identification, which will facilitate further seismic inversion and reservoir characterization.

Resolution enhancement of seismic data using spectral modeling based on dominant Ricker components and separable nonlinear least squares

Seismic resolution enhancement has a key role in seismic interpretation, especially in reservoir characterization and geologic interpretation. Exploration seismology has a limitation in temporal resolution and thin-layer detection, so any effort to improve the resolution of available data is valuable. Seismic deconvolution is one of the main steps in seismic processing and is intensively used for enhancing the vertical resolution. In this procedure, the seismic wavelet is compressed in order to decrease wavelet interferences and improve temporal resolution. In the common method of seismic deconvolution — i.e., Wiener deconvolution — the seismic wavelet is estimated by using the early part of autocorrelation of traces. Assuming the reflection series is a random function, the smoothed version of the amplitude spectrum of traces can be considered as the amplitude spectrum of the seismic wavelet. The wavelet spectrum can be properly reconstructed by the linear combination of a number of Ricker wavelets with different peak frequencies. The authors aim to introduce a novel method for resolution improvement based on fitting and finding main Ricker components of the source-wavelet spectrum. This attempt is classified under the group of the separable nonlinear least squares (SNLS), the objective function of which is a combination of linear and nonlinear functions. A variable projection method is applied to recognize the optimum peak frequency of Ricker wavelets involved in the wavelet spectrum. Our synthetic tests indicate that the SNLS algorithm is able to achieve a very close approximation of dominant Ricker components of the source spectrum. Considering the minimum phase assumption of the wavelet, the phase spectrum is also computed by Hilbert transform of the estimated amplitude spectrum. Once the amplitude and phase information are available, the deconvolution operator can be designed. Our synthetic and real tests indicate that, in comparison with Wiener deconvolution, spectral modeling based on dominant Ricker components (SMDRC) has improved the deconvolution outputs.

Seismic resolution enhancement for tight-sand gas reservoir characterization

This is a case study on the application of inverse-Q filtering to improve the resolution of 3D seismic data, for the characterization of tight-sand gas reservoirs. When seismic waves propagate through multiple tight-sand layers in the subsurface media, the energy of high-frequency components is absorbed, and the wavelet shape is distorted. Stabilized inverse-Q filtering is able to simultaneously compensate the amplitude and correct the phase of seismic waveforms. After application to 3D seismic data, the frequency bandwidth has been increased by about 10 Hz, and the width of the wavelet has been narrowed, so that we are able to identify reflections of thin sand layers clearly. Due to the phase correction in inverse-Q filtering, filtered seismic traces can match the synthetic traces at well locations. Because of the high signal-to-noise ratio with the stabilization scheme, low-amplitude zones of interest corresponding to target high-fracture areas can be easily identified, and the detail within the anomalies can also be observed. Finally, spatial variations of tight-sand layers are depicted in the inversion profile with high resolution.