Seismic Traces Research Articles

A Novel Time-frequency Representation Method: The Generalized W Transform

The time-frequency representation (TFR) of seismic data is an effective technology for the detection of hydrocarbon reservoirs. To obtain the TFR of a seismic trace, a mathematical transform tool must be used to perform the decomposition of the seismic trace. This paper introduces an effective time-frequency decomposition method: the generalized W transform (GWT). We prove that the GWT is not a strictly reversible mathematical transform. The numerical experiment of a simulated seismic trace shows that the reconstruction relative error of inverse GWT could reach up to 20%. Nevertheless, the time-frequency spectrum obtained from its forward transform is particularly suitable for identifying oil and gas reservoirs. The presented GWT has three advantages: (1) Its time-frequency spectrum has a high temporal resolution even at low frequencies; (2) The spectral centroid corresponds to the peak frequency of the wavelet; (3) It eliminates the singularity of the original W transform (WT) at the peak frequency of the waveform. Compared with the wavelet transform and the S transform, the generalized W transform can more accurately characterize reservoirs. A real data example illustrates that the GWT provides better TFR performance and can more effectively identify hydrocarbon reservoirs.

Predicting missing sonic logs with seismic constraint

Compressional and shear sonic transit time logs (DT and DTS, respectively) provide important petrophysical and geomechanical information for subsurface characterization. However, they are often not acquired in all wells because of cost limitations or borehole problems. We develop a method to estimate the DT and DTS simultaneously from other commonly acquired well logs such as gamma ray, density, and neutron porosity. Our method consists of two consecutive models to predict the sonic logs and the seismic traces at the well locations. The model predicting the seismic traces adds a spatial constraint to the model predicting the sonic logs. Our method also quantifies the uncertainties of the prediction, which come from the uncertainties of neural network parameters and input data. We train the network on four wells from the Poseidon data set located on the Australian shelf in the Browse Basin. We test the network on the other two wells from the Browse Basin. The test results indicate better predictions of sonic logs when we add the seismic constraint.

OBC shallow water de-multiple based on the principle of Fresnel diffraction

Abstract In shallow-water ocean-bottom cable (OBC) seismic data, the ineffectiveness of conventional surface-related multiple elimination (SRME) methods due to poor seabed records is addressed. This research utilizes the seismic wavefield received by multiple cables from a single shot gather to predict shallow water multiple models for that shot gather. Initially, the seismic data within a finite aperture around a seismic trace in the time domain shot gather is treated as the known seismic wavefield. This seismic wavefield is then extrapolated along the water layer to this seismic trace, following the Fresnel diffraction principle. The extrapolated data becomes the shallow water multiple model for this seismic trace. This process is repeated for each trace in the shot gather to obtain the shallow water multiple model of the entire shot gather. Forward modeling tests have shown that smaller data apertures can effectively avoid the impact of spatial aliasing on multiple model prediction. To address the overlap of primary waves and shallow water multiples in deep seismic data, which have lower dominant frequencies, the multiple model data is used as a known seismic wavefield and extrapolated along the water layer again. This produces second-order and higher-order multiple models. Applying this model to suppress multiple waves can minimize primary waves loss. This entirely data-driven approach necessitates solely water depth information, imposing no additional conditions. Both forward modeling and real seismic data testing validate the efficacy of this method in shallow water.

Automating hyperparameter optimization in geophysics with Optuna: A comparative study

AbstractDeep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two geophysics‐related deep learning examples where a grid search and Optuna framework (an automated optimization approach) were used for hyperparameter optimization. We consider two geophysical problems related to denoising seismic traces and the inversion of seismic traces for velocity information. In both cases, models created based on Optuna hyperparameter optimization were able to perform better than those created through grid search. The most significant advantage of Optuna, however, is having quantifiable results to justify the choice of a neural network architecture, depth and other hyperparameters rather than relying on inefficient methods of exploring the hyperparameter space such as a trial‐and‐error or grid search. This study aims to stimulate further exploration and adoption of these frameworks, pushing the boundaries of current deep learning based geophysical problem‐solving methodologies towards full automation.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

Novel techniques for in situ estimation of shear-wave velocity and damping ratio through MASW testing – I: a beamforming procedure for extracting Rayleigh-wave phase velocity and phase attenuation

SUMMARY A robust, in situ estimate of shear-wave velocity VS and the small-strain damping ratio DS (or equivalently, the quality factor QS) is crucial for the design of buildings and geotechnical systems subjected to vibrations or earthquake ground shaking. A promising technique for simultaneously obtaining both VS and DS relies on the Multichannel Analysis of Surface Waves (MASW) method. MASW can be used to extract the Rayleigh wave phase velocity and phase attenuation data from active-source seismic traces recorded along linear arrays. Then, these data can be inverted to obtain VS and DS profiles. This paper introduces two novel methodologies for extracting the phase velocity and attenuation data. These new approaches are based on an extension of the beamforming technique which can be combined with a modal filter to isolate different Rayleigh propagation modes. Thus, the techniques return reliable phase velocity and attenuation estimates even in the presence of a multimode wavefield, which is typical of complex stratigraphic conditions. The reliability and effectiveness of the proposed approaches are assessed on a suite of synthetic wavefields and on experimental data collected at the Garner Valley Downhole Array and Mirandola sites. The results reveal that, under proper modelling of wavefield conditions, accurate estimates of Rayleigh wave phase velocity and attenuation can be extracted from active-source MASW wavefields over a broad frequency range. Eventually, the estimation of soil mechanical parameters also requires a robust inversion procedure to map the experimental Rayleigh wave parameters into soil models describing VS and DS with depth. The simultaneous inversion of phase velocity and attenuation data is discussed in detail in the companion paper.

Seismic Station Monitoring Using Deviation from the Gaussianity

AbstractDegradation of the seismic signal quality sometimes occurs at permanent and temporary stations. Although the most likely cause is a high level of humidity, leading to corrosion of the connectors, environmental changes can also alter recording conditions in different frequency ranges and not necessarily for all three components in the same way. Assuming that the continuous seismic signal can be described by a normal distribution, we present a new approach to quantify the seismogram quality and to point out any time sample that deviates from this Gaussian assumption. We introduce the notion of background Gaussian signal (BGS) to characterize a set of samples that follows a normal distribution. The discrete function obtained by sorting the samples in ascending order of amplitudes is compared with a modified Probit function to retrieve the elements composing the BGS, and its statistical properties (mostly its standard deviation σG). As soon as there is any amplitude perturbation, σG deviates from the standard deviation of all samples composing the time window (σ). Hence, the parameter log(σσG) directly quantifies the alteration level. For a single day, a given frequency range and a given component, the median of all log(σσG) that can be computed using short-time windows, reflects the overall gaussianity of the continuous seismic signal. We demonstrate that it can be used to efficiently monitor the quality of seismic traces using this approach at four broadband permanent stations. We show that the daily log(σσG) is sensitive to both subtle changes on one or two components as well as the signal signature of a sensor’s degradation. Finally, we suggest that log(σσG) and other parameters that are computed from the BGS bring useful information for station monitoring in addition to existing methods.

Application of AVO Characteristics Analysis and Seismic Dispersion AVO Inversion to the Carbonate Hydrocarbon Reservoirs in Region Y of the Tarim Basin, China

In recent years, breakthroughs in deep hydrocarbon exploration have been continuously achieved in the Tarim Basin of China. The Ordovician carbonate stratum has been shown to contain vast oil and gas resources. However, challenges remain in understanding the seismic response characteristics and accurately identifying the hydrocarbon reservoirs. These challenges can be attributed to factors including the significant burial depth of the target layers and complex geologic structures. To effectively support hydrocarbon exploration and resource assessment in the Tarim Basin, this study focused on the following three aspects in Region Y. First, according to the analysis of prestack amplitude versus offset (AVO) characteristics in well‐adjacent seismic traces and seismic forward modeling, we demonstrated that different fluid‐filled reservoirs have distinctly different AVO characteristics. This means that the hydrocarbon reservoirs in the study area can be identified by AVO inversion. Second, based on the theory that seismic waves exhibit amplitude attenuation and velocity dispersion when propagating through fluid‐filled media, the seismic dispersion AVO inversion technique was developed to obtain equations for the attributes of primary (P) and shear (S) seismic wave dispersion. Finally, this technique was applied in Region Y and further verified using actual drilling data from single wells and well profiles. The application results demonstrate that hydrocarbon reservoirs can be effectively identified using this technique and provide a technical reference for reservoir identification in other areas of the Tarim Basin. The AVO characteristic analysis is the prerequisite for the successful application of this method; the key is to find accurate inversion equations and parameters to recognize the AVO response patterns in the study area.

Vectorising Analog Seismograms by Techniques of Machine Learning for Automated Discriminating of Seismic Signal Traces

Vectorising Analog Seismograms by Techniques of Machine Learning for Automated Discriminating of Seismic Signal Traces

Application of Seismic Attribute Analysis Technology Guided by Model Forward Modeling in L29 Area

Taking the SaErTU and Putaohua oil layers in the L29 well area of LHP oilfield in the northern Songliao Basin as the research object, In response to the difficulty of interference between thin interbedded sand and mudstone and strong reflection between strata, which have a significant impact on fine prediction of sand bodies, a stratigraphic model is established based on the geological characteristics of the target layer to eliminate the impact of stratigraphic reflection; Then add a thin layer of sand body to its interior, establish a thin interlayer model, and obtain a geological model that is more in line with the actual situation of the target layer. Using forward simulation, analyze the seismic response characteristics of sand bodies, extract 30 seismic attributes from 4 categories: amplitude statistics, composite seismic trace statistics, sequence and frequency spectrum statistics, and calculate the correlation between cumulative sandstone thickness and seismic attributes, and select a sensitive seismic attribute set;By combining sedimentary and drilling data, the seismic attribute with the highest sensitivity to the target layer is selected. The multi-level dimensionality reduction and gradual improvement of seismic attribute selection methods using “model forward modeling, attribute analysis, relevant optimization, and drilling implementation” can effectively improve the prediction accuracy of thin interbedded sand bodies and greatly reduce the risk of oilfield exploration and development.

Application of geophysical techniques based on multi-attribute inversion in predicting high-quality source rocks

Total organic carbon (TOC) content is an important indicator for evaluating the quality of source rocks. Conventional laboratory analysis and testing, single well interpretation, and other methods are limited by sample data or the number of well locations, and cannot comprehensively and accurately predict the planar distribution of TOC content in source rocks. Based on this, a method for predicting TOC content using seismic inversion with multiple attributes is proposed. The basis for applying seismic attributes to study TOC of source rocks is the inherent relationship between seismic and logging data. By using logging data to calculate TOC, the correlation between seismic attributes of well side seismic traces and logging is established, and seismic attributes are converted into TOC of source rocks. The results show that the TOC content in the third section of the Shahejie Formation of the Liaohe Depression is relatively high, distributed in the range of 1% to 5%. The trend of planar variation is manifested as a gradual decrease from the centre of the depression to both sides, and the centre is concentrated in the Panshan and Qingshui area. The overall prediction effect is good, proving the strong feasibility of this method.

Reconstructing Regularly Missing Seismic Traces With a Classifier-Guided Diffusion Model

Reconstructing Regularly Missing Seismic Traces With a Classifier-Guided Diffusion Model

Complex spherical-wave seismic inversion in anelastic media for the P-wave minimum quality factor

Attenuation always exists when seismic waves propagate in underground anelastic media, especially in hydrocarbon-bearing reservoirs. Quality factor Q or attenuation factor 1/ Q can be used to quantify the seismic wave attenuation and has become an important hydrocarbon indicator. The relationship between the plane-wave reflection coefficient ([Formula: see text]) in anelastic media and P- and S-wave quality factors has been widely used in the plane-wave seismic inversion to estimate the quality factors. The [Formula: see text] provides an adequate approximation for the deeper subsurface. However, for the shallow subsurface and anelastic wavefields excited by point sources, the [Formula: see text] is inaccurate and its meaning involves some fundamental difficulties. In view of this, a Q-dependent P-P spherical-wave reflection coefficient ([Formula: see text]) in anelastic media is used here. Considering that having too many parameters to be inverted will lead to unstable and inaccurate inversion results, we further derive an approximate anelastic [Formula: see text] and anelastic spherical-wave impedance ([Formula: see text]), which are frequency dependent and are the functions of P- and S-wave velocities, density, and P-wave minimum quality factor ([Formula: see text]). Finally, a complex spherical-wave seismic inversion approach in anelastic media for the P-wave minimum quality factor is developed. Using the Bayesian inversion approach and complex convolution model, we first estimate the multilayer [Formula: see text] from the complex seismic traces with different frequencies and incidence angles. Based on the inverted angle- and frequency-dependent [Formula: see text], the P- and S-wave velocities, density, and P-wave minimum quality factor are further estimated using a nonlinear inversion tool. Synthetic examples verify the feasibility and robustness of the complex spherical-wave seismic inversion approach in anelastic media. In the shallow subsurface, the spherical-wave inversion is superior to plane-wave inversion. A field example further demonstrates the accuracy and great potential of our approach in hydrocarbon-bearing reservoir prediction.

Achieving a Comprehensive Microseismicity Catalog through a Deep-Learning-Based Workflow: Applications in the Central Ecuadorian Subduction Zone

ABSTRACT Although seismological networks have densified along the Ecuadorian active margin since 2010, visual phase reading, ensuring high arrival times quality, is more and more time-consuming and becomes impossible to handle for the very large amount of recorded seismic traces, even when preprocessed with a detector. In this article, we calibrate a deep-learning-based automatized workflow to acquire accurate phase arrival times and build a reliable microseismicity catalog in the central Ecuadorian forearc. We reprocessed the dataset acquired through the OSISEC local onshore–offshore seismic network that was already used by Segovia et al. (2018) to produce a reference seismic database. We assess the precision of phase pickers EQTransformer and PhaseNet with respect to manual arrivals and evaluate the accuracy of hypocentral solutions located with NonLinLoc. Both the phase pickers read arrival times with a mean error for P waves lower than 0.05 s. They produce 2.7 additional S-labeled picks per event compared to the bulletins of references. Both detect a significant number of waves not related to seismicity. We select the PhaseNet workflow because of its ability to retrieve a higher number of reference picks with greater accuracy. The derived hypocentral solutions are also closer to the manual locations. We develop a procedure to automatically determine thresholds for location attributes to cull a reliable microseismicity catalog. We show that poorly controlled detection combined with effective cleaning of the catalog is a better strategy than highly controlled detection to produce comprehensive microseismicity catalogs. Application of this technique to two seismic networks in Ecuador produces a noise-free image of seismicity and retrieves up to twice as many microearthquakes than reference studies.

Seismic geomorphology of the deepwater sediment dispersals systems in the drift phase of the Mundaú sub-basin, equatorial margin, Brazil

The Ceará Basin, located in the Brazilian Equatorial Margin, represents an exploratory frontier situated in a complex structural framework. The basin observed a dynamic interplay of extensional, transtensional, and transpressional regimes, registered by several sedimentary intervals and unconformities poorly documented by wells, especially at the deeper portions. In this study, based on 3D seismic and well data from the Mundaú Sub-Basin, we interpreted the main seismic sequences of the drift phase to build a Relative Geological Time (RGT) model, based on a semi-automatic seismic traces correlation matched with the well top formations. A seismic attribute analysis from key seismic surfaces extracted from the RGT, highlights the main sediment dispersal routes revealing a significant change after the Mid-Eocene, once sedimentation overpassed a topographic barrier created by fault reactivations, in a transpressional regime, associated with the Romanche Fracture Zone. The integration of seismic and well data affords a deeper understanding of basin dynamics, revealing how sedimentary processes respond to shifts in tectonic stress in this portion of the basin.

Seismic Data Interpolation via Improved cGAN Network

The seismic data are always spatially undersampled during filed acquisition, limited by the natural environment and economic conditions. Irregularly missing seismic traces can affect the subsequent processing of seismic data, ultimately impacting the imaging and inversion results. Therefore, seismic data regularisation and interpolation are particularly necessary and have become an important step for data pre-processing. This abstract presents an improved GC algorithm with partial convolution layers and spectral normalization and applies it on 2D seismic data reconstruction. Compared with the traditional cGAN reconstruction method, The proposed method can effectively enhance the generative capability of the cGAN algorithm. Both synthetic and field data experiments demonstrate that the reconstruction performance of the improved cGAN method is better than the conventional cGAN method.

Reverse time migration surface offset gathers by attribute migration and constrained least-squares inversion

Abstract Surface offset gathers (SOGs) are crucial for velocity updating in seismic data migration and muting the stretched waveform in shallow parts of migrated seismic data. Kirchhoff-based migration methods can output SOGs because they take every seismic trace as the input. As a result, the imaging results can be rearranged according to the offset value of the input data to obtain SOGs. However, regarding more accurate wave equation methods (such as reverse time migration, RTM), the SOGs cannot be obtained directly with a single migration calculation. Attribute migration is an easy-to-implement method to output SOGs of wave equation migration methods. It calculates the offset value of each imaging point by dividing the migration result modulated by the offset attribute with the conventional migration result. However, the division error may cause the calculation result outside the given offset value. This paper proposes a constrained least-squares inversion method for stable calculation of the RTM offset range. The method ensures that the calculated results are within the given offset range. We tested the method against the direct division and least-squares methods without constraints using the Marmousi model and a real 3D dataset. We show that the SOGs obtained by the constrained least-squares inversion attribute migration method had the same characteristics as those derived using the division-based attribute migration method, but they also had higher vertical resolution, better energy distribution, and improved lateral continuity. In a further study, we expect to develop a stable and direct method to efficiently calculate SOGs for RTM and an iterative closed loop for RTM velocity updating.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16 seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model. Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2 is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

Seismic Data Reconstruction based on Attention U-net and Transfer Learning

In field seismic exploration, missing seismic traces is inevitably encountered due to the constraints of the exploration environment and equipment. Thus, seismic data reconstruction is essential for seismic exploration analysis. In this paper, an attention-based U-net (AU-net) architecture is proposed by incorporating the attention mechanism into the U-net structure to address seismic data reconstruction challenges. The network incorporates both channel and spatial attention modules. Experiments comparing the reconstruction results of U-net, DnCNN, curvelet, and AU-net demonstrate the robustness of deep learning methods in handing increased missing trace percentages. The AU-net achieves superior reconstruction effect on contiguous missing regions, and exhibits better generalization through transfer learning experiments. To facilitate network training, this paper introduces a novel data-slicing technique to split and merge rectangular data for reconstructing shot records. This approach can be applied to any network and has high practical value.

A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm

Seismograms are a vital source of information in seismic signal processing. These records are contaminated by noise, which should be reduced before processing in seismic applications. Wavelet-based methods have shown great success in denoising seismic signals, and their performance is determined based on the wavelet transform (WT) parameters. Therefore, in this paper, four swarm optimization algorithms are suggested to estimate the WT parameters optimally to mitigate the noise in the seismic signal. First, this article examines the efficacy of the optimization algorithms for achieving the minimum fitness value and better convergence curve. Then, based on the fitness value, optimal wavelet transform parameters are obtained to perform discrete wavelet transform on the seismic signal. The analysis is conducted on seismic trace and seismic section to disclose the dominance of the proposed algorithm over the traditional approaches. The results demonstrate the effectiveness of our proposed strategy for seismic signal denoising over other traditional approaches.