Seismic Traces Research Articles

Seismic post-stack impedance inversion using geophysics-informed deep learning neural network

Traditionally, seismic post-stack impedance inversion is implemented using linear optimization algorithms. Recently, deep learning neural networks have been successfully used to estimate the impedance from seismic data. First, we demonstrate the general workflow of seismic post-stack impedance inversion using supervised neural network (SNN). Next, we propose to compute seismic impedance using geophysics-informed neural network (GINN). Similarly with linear optimization algorithms, the inputs of GINN include real seismograms, a wavelet, and a low frequency model. The loss function of GINN is designed to minimize the difference between real seismograms and synthetic seismic seismograms that are computed from estimated impedance and input wavelet. To avoid lateral discontinuity of estimated impedance, the weights of GINN are trained using the seismograms of all seismic traces. We use synthetic and real seismic data to discuss the advantages and limitations of GINN, SNN, and traditional linear optimization algorithms. Not surprisingly, signal-to-noise ratio (SNR) of seismic data and the phase of seismic wavelet are the most important factors that affect the accuracy of impedance estimated using GINN. The accuracy and resolution of impedance estimated using GINN is higher than linear optimization and SNN if the seismic data has a high signal-to-noise ratio. The synthetic examples demonstrate that the accuracy of impedance calculated using SNN increases with the number of available training wells and linear optimization algorithms are more robust to noise than GINN and SNN.

Generating more constrained seeds for seismic horizon extraction

Enhancing accuracy remains the main challenge for the current horizon extraction algorithms. Adding more manually interpreted seeds helps to improve the accuracy of horizon extraction. We intend to generate more seeds prior to the 3D horizon extraction based on seeds that are manually interpreted on the coarse grid of the seismic survey. First, we compute four horizons for the same seismic sequence boundary using deep learning and constrained least-squares algorithms under the constraints of manually interpreted horizon seeds. Next, we generate more seeds by checking the consistency among those four horizons for each seismic trace, and this process is implemented in the same way as the tying loops of manual horizon interpretation. Finally, we extract horizons using constrained least-squares algorithms with the newly computed seeds. The applications of two real seismic surveys demonstrate that our method can generate horizons with higher accuracy when compared with the horizons computed with only manually interpreted seeds.

Seismic fault detection with sliding windowed differential cepstrum–based coherence analysis

AbstractCepstral decomposition is beneficial for highlighting certain geological features within the particular quefrency bands which may be deeply buried within the wide quefrency range of the seismic data. Converting seismic traces into the corresponding cepstrum components can better analyse some characteristics of underground strata than the traditional spectral decomposition methods. We propose the sliding windowed differential cepstrum–based coherence analysis approach to delineate the fault features. First, the data are decomposed using a sliding windowed differential cepstrum, which results in multi‐cepstrum data of corresponding quefrency of certain bandwidth. These different multi‐cepstrum data may highlight the different stratigraphic features in a certain quefrency band. We select the first‐order common quefrency volume as the featured attribute. Then, eigenstructure‐based coherence is applied on the first‐order common quefrency data volume to statistically obtain the fault detection result with a finer and sharper image. Synthetic data and field data examples show that the proposed method has the ability to better visualize all the possible subtle and minor faults present in the data more accurately and discernibly than the traditional coherence method. Compared with the ant‐tracking method, the proposed method is more effective in revealing the major faults. It is hoped that this work will complement current fault detection methods with the addition of the cepstral‐based method.

Seismic trace interpolation based on the principle of reciprocity using a conditional Generative Adversarial Network (cGAN)

Seismic trace interpolation based on the principle of reciprocity using a conditional Generative Adversarial Network (cGAN)

Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation

AbstractEstimation of the porosity of a hydrate reservoir is essential for its exploration and development. However, the estimation accuracy was usually less certain in most previous studies that simply assumed that there is a linear relationship between the porosity and single‐elastic wave velocities or other rock physical parameters, thus affecting the evaluation of the reserves. In the three‐phase Biot‐type equations that are fundamental to model a hydrate‐bearing reservoir, porosity, alongside hydrate saturation, mineral constituent proportions and hydrate–grain contact factor, is non‐linearly responded by density, compressional and shear wave velocities. To improve porosity estimation, we propose to invert simultaneously four‐parameter (porosity, hydrate saturation, mineral constituent proportions and hydrate–grain contact factor) using an iteratively nonlinear interior‐point optimization algorithm to solve a nonlinear objective function that is a summation of the squared misfits between the well log and three‐phase Biot‐type equation–modelled density, compressional and shear wave velocities. A test in Mount Elbert gas hydrate research well was conducted for the case of a gas hydrate stratigraphic test well where elastic wave velocities, density, porosity and mineral composition analysis data are available. The four‐parameter inversion yielded a lower root mean square error for porosity (0.0245) across the entire well‐logging section compared to previous estimations from the linear relationship, post‐stacked and pre‐stacked seismic traces as well as the pore‐filling effective medium theory model applied to other well cases. Additionally, the other three parameters demonstrated good agreement with well logs. Inversion tests conducted at three additional hydrate sites also produced accurate results. Consequently, the new method surpasses previous approaches in porosity estimation accuracy.

A Deep Learning Structure Correction Method Under the Influence of Special Lithology Body

Abstract In the process of precise exploration and development of oil and gas reservoirs, the accurate implementation of the target layer structure plays a vital role in the increase of oil reserves and the success of drilling. However, with the gradual deepening of the research, it is found that in the overlying strata of oil and gas reservoirs, special lithological bodies with strong heterogeneity and abnormal velocity are usually locally developed, which directly affects the construction of seismic data migration velocity, resulting in structural distortion at the local position of the reservoir in the process of data interpretation. Focusing on the structural change caused by the abnormal velocity, the deep learning algorithm is used to accurately predict the velocity of the special lithologic body and realize the accurate correction of the structure. Firstly, based on the combination of seismic and geological understanding, the geological model of the target layer and the overlying special lithologic body is constructed, and the distribution law of the special lithologic body is identified by the forward simulation method. Then use the well-side seismic trace and P-wave velocity curve to train the velocity data through the deep learning method and apply the training results to the post-stack seismic data to obtain the accurate P-wave velocity body. Finally, the real formation velocity over the target layer is restored to achieve reservoir structure correction. Based on this method, the research experiment was carried out in the M block of the Tarim Basin, and the spatial distribution range of the special lithology body was carved. The data error between the migration velocity body and the abnormal high-velocity body was calculated, and the target layer structure in the study area was corrected.

Horizon picking algorithm using global optimization and coherence measurement in poststacked seismic data

Accurate horizon recognition within poststack seismic sections is important in seismic interpretation. Horizon picking techniques influence diverse seismic structural analyses and inversion methodologies. Despite encountering challenges such as computational demands and time constraints, the past few years have witnessed the development of numerous 2D and 3D methods. With the progress of modern computing, more robust and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we develop an innovative cost-effective algorithm grounded in a global optimization framework. This algorithm combines the very fast simulated annealing method with a set of stability parameters and coherence measurements between neighboring seismic traces, which is used as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm’s efficacy by applying it to two distinct input data sets — enveloped and nonenveloped seismic traces. The initial application is to the time-migrated Marmousi data set, a synthetic marine data set originated from a complex geologic sedimentary basin situated in Angola, Africa. Subsequently, we apply the algorithm to depth-migrated land data extracted from a past survey conducted in the Tacutu Basin in northern Brazil. Outcomes arising from the application to 2D synthetic and field data sets underscore the method’s viability as a compelling alternative for seismic horizon picking within the time or depth domain.

Reservoir oriented migrated seismic image improvement and poststack seismic inversion using well-seismic mistie

In seismic exploration of subsurface hydrocarbon reservoirs, migrated seismic image is the foundation for structural interpretation and subsequent seismic inversion for reservoir properties. Seismic events in migrated images can be mispositioned due to several factors including seismic velocity inaccuracy. The mispositioned events lead to poor well-seismic tie and degrades the accuracy of subsequent inversion and reservoir prediction. Particularly, the mistie can be quite obvious for the mid- and mature- phases of the reservoir development after many wells drilled. We propose to utilize the information from mistie between seismic images and synthetic traces to improve the accuracy of migrated seismic images and thus inversion results. Our method includes three major steps: firstly, calculate time shifts between the synthetic traces and the co-located seismic trace using the path-limited dynamic time warping (PDTW) algorithm; then compute the time shift volume for the entire migrated image using the seismic-driven modeling approach based on the time shift traces at the well locations; apply the time shift volume to update the migrated image. The improved seismic volume can be used for further processing such as seismic inversion, which makes the inversion results more consistent with seismic and well data. We apply our method to synthetic and field datasets. The results demonstrate that the updated seismic image has an improved seismic well tie and an improved inversion result. Our method indicates a venue for a better honor of seismic and well data in reservoir characterization.

The minimum-frequency property of complex trace analysis

A waveform has the property of minimum frequency if its instantaneous frequency equals the Hilbert transform of its instantaneous bandwidth plus the spectral low-cut frequency. Minimum frequency is characterized by an amplitude spectrum that is maximally skewed toward the low frequencies. The associated property of maximum frequency is characterized by an amplitude spectrum that is maximally skewed toward the high frequencies. Minimum and maximum frequency are the time-domain counterparts of the spectral properties of minimum and maximum delay. Many waveforms have the property of minimum frequency, such as the Ricker wavelet. Waveforms that have the property of maximum frequency include Klauder wavelets that emphasize high frequencies. Impulsive wavelets can have both minimum frequency and minimum delay. The properties of minimum and maximum frequency suggest time-variant measures to estimate the spectral low-cut and high-cut frequencies of seismic traces and produce an instantaneous frequency attribute that is free of spikes. The property of minimum frequency has long been known but remains unfamiliar to geophysics. It is explained in the context of complex trace analysis illustrated with seismic wavelets and traces.

Sparse-spike seismic inversion with semismooth newton algorithm solver

Seismic prospecting has been widely used in the exploration and development of underground geological resources, such as mineral products (e.x., coal, uranium deposit), oil and gas, groundwater, and so forth. Seismic impedance is a physical characteristic parameter of underground formation, which can be used in lithologic classification, rock characterization, stratigraphic correlation, and further mineral reservoir prediction, reserve estimation, and so forth. To estimate impedance from seismic data, one must perform reflectivity series inversion first. Under a simple exponential integration transformation, the reflectivity series can give the final estimated impedance. Sparse-spike seismic inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on reflectivity series. From sparse reflectivity series, the final estimated impedance has blocky features to make formation interfaces and geological edges precise, which is very important to accurately delineate the distribution range of mineral resources. The development of sparse-spike seismic inversion is still facing major challenges of fast optimization algorithms in real-life application, especially for massive seismic data in 3D case. Semismooth Newton algorithm (SNA), which is a second order mehtod and has super-linear, even quadratic convergence rate, is used to solve sparse-spike seismic inversion. The proposed algorithm has been compared with common used algorithms through a synthetic seismic trace and a 3D real seismic data volume. The results show that the proposed algorithm has faster convergence rate and fewer computation time. It provides a new effective algorithm to solve sparse-spike seismic inversion.

A combined denoising method for Q-factor compensation of poststack seismic data

Attenuation is a main factor limiting the resolution of seismic data. Earth works as a low-pass filter, which has strong attenuation of the high-frequency data. The loss of high-frequency energy can be compensated by the inverse Q filtering strategy. However, this method will also increase the energy of random noise which limits its application. The inverse Q filtering algorithm also needs the Q-factor as the input parameter, which is not easy to obtain. In this paper, we proposed a three-stage process to correct the attenuation of poststack data. In the first stage, a robust structure-oriented filtering is applied to remove random noise while protecting the structure information to avoid high-frequency noise burst. In the second stage, the local centroid frequency shift (LCFS) method is used to estimate the Q factor along the seismic trace. This method combined shaping regularization and centroid frequency shift (CFS) method to improve the robustness and accuracy of Q estimation to some extent. The final stage is to apply a stable inverse Q-filtering. Synthetic and field data examples demonstrate that time-varying Q-value can be accurately estimated by using the local centroid frequency shift (LCFS) method, and the proposed workflow can compensate the attenuation without bursting of high-frequency random noise.

Full‐waveform inversion as a tool to predict fault zone acoustic properties

AbstractUnderstanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three‐dimensional seismic reflection data can image the geometries of faults in the sub‐surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale‐gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full‐waveform inversion P‐wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high‐quality post‐stack depth–migrated seismic reflection and full‐waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post‐stack depth migrated and full‐waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full‐waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P‐wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P‐wave velocity anomalies within fault zones and the modern‐day stress orientation. Faults with high P‐wave velocity are the ones that are perpendicular to the present‐day maximum horizontal stress orientation and are likely under compression. Faults with lower P‐wave velocity are the ones more parallel to the present‐day maximum horizontal stress orientation and are likely in extension. We propose that these P‐wave velocity anomalies may indicate differences in how ‘open’ and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower VP) and therefore may provide a promising proxy for fault transmissibility.

An unsupervised machine learning algorithm approach using K-Means Clustering for optimizing Surface Wave Filtering in seismic reflection data

Surface waves often cause significant noise in seismic data, complicating the interpretation of subsurface structures. Traditional filtering methods, such as FK filtering, usually struggle with non-stationary noise and require extensive manual parameter tuning. This study explores the effectiveness of using K-means clustering, incorporating attributes such as amplitude, frequency, and phase to filter surface waves from seismic data. Synthetic seismic data were first generated to test the proposed method, ensuring its robustness before application to real field data. Attributes were extracted from each seismic trace, including instantaneous amplitude, frequency, and phase. These attributes were used as input parameters for the K-means clustering algorithm. The identified clusters corresponding to surface waves were then used to filter these waves from the seismic data. The K-Means clustering effectively differentiated surface waves from reflected waves in both synthetic and real seismic datasets. The method demonstrated that by including phase as an attribute, alongside amplitude and frequency, the accuracy of surface wave detection and filtering significantly improved. The synthetic data showed a clear separation of wave types, validating the method. When applied to real field data, the approach consistently removed surface waves, clarity of seismic reflections crucial for subsurface analysis.

Dealiased seismic data interpolation by dynamic matching

Interpolation is a critical step in seismic data processing. Gaps in seismic traces can lead to severe spatial aliasing phenomena in the corresponding f- k spectra. The aliasing caused by regularly spaced gaps has similar f- k spectra as those of the actual data. Existing dealiasing interpolation algorithms generally assume that seismic events are linear and cannot handle nonstationary events. To address this shortcoming, we develop a novel dealiased seismic data interpolation approach using dynamic matching. First, we match two adjacent seismic traces using the local affine regional dynamic time-warping algorithm. Subsequently, we calculate the local slope between two seismic traces. Finally, we perform linear interpolation on the regularly missing seismic data using local slope information. Our approach is tested on synthetic and field seismic data sets. The interpolation results indicate that our approach has a better antialiasing ability and computational efficiency than the traditional Spitz and seislet-based approaches. In addition, this method can be applied to interpolate irregularly sampled seismic data and for simultaneous seismic data interpolation and denoising.

A Comparison of U-Net with Conditional Generative Adversarial Networks and Cycle-Consistent Adversarial Networks for real seismic data interpolation: Tupi Field

Deep learning models have been used to improve seismic trace interpolation in recent years. Encode-Decode models, such as U-Net, have been implemented to solve interpolation problems. The success of U-Net in seismic interpolation has inspired us to test the U-net also as an image generator for further Generative Adversarial Network (GAN) interpolation models. The objective of the present paper is to compare the performance of U-Net inside the GAN models for seismic interpolation: the U-Net alone and two GAN models, the conditional GAN (cGAN) and the Cycle Consistent GAN (CycleGAN), both using the U-Net as a generator inside their workflow. We test the methodologies for two scenarios: regular and irregular interpolation. All tests were performed in a real dataset from the Tupi Field which belongs to the Brazilian pre-salt region. A comparison of the statistical metrics shows that cGAN performs better than CycleGAN and the U-Net alone in most cases. The computational training time of the cGAN model, for all interpolation scenarios, is better than the CycleGAN. Finally, the cGAN training time is comparable to the training of the U-Net alone.

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System.

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

A comparative study on hydrocarbon detection using cepstrum–based methods

Amplitude anomaly caused by a high–frequency component is attenuated more rapidly than a low–frequency component for a seismic trace in sediments susceptible to gas influence and is always used as a direct indicator of hydrocarbons. Recently, cepstrum–based hydrocarbon detection methods have become an important exploration pathway for detecting amplitude anomalies in the gas–bearing formation more sensitively and accurately. In this study, we compared three cepstrum–based hydrocarbon detection methods. We compared their fluid identification abilities in gas reservoirs and the noise robustness. They were further compared with the traditional absorption coefficient estimation method. We also indicate how the choice of the selection of the sliding–window length influences the performance of the three cepstrum–based hydrocarbon detection methods. Model test results and real data applications show that for thick gas reservoirs, the hydrocarbon detection methods using the Berthil–based cepstrum and the wavelet–based cepstrum can better discriminate the upper and lower interfaces than the hydrocarbon detection method using the Fourier–based cepstrum. For thin gas reservoirs, the hydrocarbon detection method using the wavelet–based cepstrum can identify the lower interface of the gas–bearing reservoir more obviously than the upper interface. The hydrocarbon detection method using the Berthil cepstrum cannot identify the upper and lower interfaces of the gas–bearing reservoir, but it shows higher temporal and spatial resolution compared to the hydrocarbon detection method using the Fourier–based cepstrum. For noise robustness, the hydrocarbon detection method using the wavelet–based cepstrum has the strongest noise robustness property, and the hydrocarbon detection method using the Fourier–based cepstrum is the most sensitive method to the noise. For time–consumption, the hydrocarbon detection method using the wavelet–based cepstrum is the most time–consuming, and the hydrocarbon detection method using the Fourier–based cepstrum is the fastest.

Accessing Horizon Picking Uncertainty using Seismic Complex Trace Attributes

Many uncertainty sources are linked to the reservoir modeling processes, significantly affecting on all phases of the exploration, development, and production phases of an oil & gas field. Structural uncertainty due to velocity models are commonly considered by taking a range of equiprobable domain conversion velocity models and evaluating the related impact on GRV distributions. In contrast, the seismic interpretation inaccuracy is commonly neglected, because the difficulty in quantifying it. This means that seismic mapping is frequently treated as having no variation or having the intrinsically imprecisions assigned arbitrarily. In this work, we propose an objective approach to evaluate the horizon picking uncertainties, which was based on the complex seismic trace attributes extraction considering a previously mapped reference reflector. In real seismic data, phase distortions may arise from a vast bunch of reasons, such as dispersion, attenuation, bandwidth limitations. Thus, the instantaneous phase attribute can be an indicator of uncertainties associated with seismic reflectors interpretation.

A depth-variant wavelet extraction method based on the orthogonal matching pursuit for direct inversion

The results of pre-stack depth migration reflect subsurface structures directly. However, it is an urgent geophysical problem that how to deal with this non-stationarity exhibited by depth-domain seismic data and achieve high precision reservoir prediction. To address this issue, we develop a depth-variant wavelet extraction method based on the orthogonal matching pursuit and attain direct inversion of the depth-domain acoustic impedance using the depth-variant wavelets. First, the parameters of source wavelets estimated by complex seismic trace analysis are utilized to construct the overcomplete dictionary. Secondly, the overcomplete dictionary is used to decompose the depth-domain seismic traces by orthogonal matching pursuit. Thirdly, we extract the depth-variant Ricker wavelets from the decomposed optimal atoms. Test of examples demonstrates that the proposed depth-variant wavelet method has high accuracy. The synthetic seismogram obtained by the proposed method has high Pearson correlation coefficients of 99%, which verifies that our method is feasible and accurate. Next, a depth-domain impedance trend constraint is introduced into the objective function of the conventional basis pursuit inversion to enhance the lateral continuity. Finally, the objective function with impedance trend constraint is solved by basis pursuit for depth-domain acoustic impedance. We test the direct depth-domain acoustic impedance inversion method on the synthetic and field data, which indicates that our method has high accuracy and robustness. The mean relative error of its inversion result is only 0.9% for the noise-free example and about 5% for the strong noisy example. Therefore, our method can be effectively applied to the seismic-well calibration and seismic impedance inversion in the depth domain. And it has powerful potential in reservoir characterization, fluid prediction and attribute extraction in the depth domain. Additionally, the energy distribution features of the Ricker wavelet are derived and analyzed, which can guide the application of the Ricker wavelet in seismic signal analysis.

Multichannel deconvolution with a high-frequency structural regularization

The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic records. Sparse spike inversion, as an important processing method, can effectively improve the band-limited property of the seismic data. However, this approach ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this paper, we develop a high-frequency structure-constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of a prediction-error filter (PEF) to regularize the components of the result beyond the original frequency. The PEF, also called a high-frequency reflection structural characterization operator, is estimated from the mapping relationship of low- and high-frequency components. We adopt the alternating direction method of multipliers to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that our method recovers more reliable high-resolution data and enriches reflective structures.