Pre-stack Data Research Articles

A Comparison of the Classical OWWE Migration Methods: Analysis of Feasibility in Subsalt Plays Exploration

Subsalt seismic imaging is particularly difficult due to the complex overburdens, caused by the movement of salt that usually results in steeply dipping structures, which along with strong lateral velocity contrast at the sedimentssalt interface distort the structural position and the stratigraphic resolution of the subsalt reservoirs. Despite it was proven that two-way wave equation migration provides the best results illuminating these reservoirs, it has huge computational cost, especially in three dimensions. One-way wave equation (OWWE) migration techniques are good alternative in this case as providing the acceptable quality of seismic sections with an appropriate computational cost. To know the advantages and limitations of the OWWE techniques in subsalt imaging, three classical OWWE algorithms were evaluated for depth migration of prestack data (PSDM): phase-shift-plus-interpolation (PSPI), splitstep Fourier (SSF), and Fourier finite-difference (FFD). These algorithms were tested with three different fully elastic synthetic models which simulates the structural complexity showed in subsalt plays. It was concluded that FFD gives very accurate results when the lateral velocity variation was strong with acceptable computational cost. The PSPI provided the best quality results but required about twice the computer time needed for FFD, and SSF was the fastest but clearly the least accurate.

3-D prestack data preconditioning for seismic amplitude inversion and the estimation of geomechanical properties: A case study from the structurally complex Moncton sub-basin, Eastern Canada

3-D land seismic data collected in the Moncton sub-basin (Eastern Canada) are plagued by random and coherent nose attributed to difficult acquisition conditions and complexities in the subsurface. Prior to amplitude inversion or the estimation of geomechanical properties, data must be preconditioned to provide reliable results. The preconditioning approach includes AVO-friendly rank reduction filtering followed by frequency-wavenumber (f-k) trace regularization. First, a synthetic model is used to asses the ability of the rank reduction approach to preserve the AVO behavior under various noise conditions before it is applied to the field data. The rank reduction scheme more than doubles the signal-to-noise ratio (s/n) of near-offset and mid-offset traces, increases the s/n of far-offset traces by 1.5 times and reduced amplitude distortions by removing the coherent noise from the data. F-k trace regularization helped to fill gaps in the angle coverage induced by irregular and wide-spaced offsets. However, the modeling experiment showed that the performance of rank reduction decreases as the s/n deteriorates. For low s/n, the model suggests that noise significantly alters the AVO behavior. This makes it very challenging for the filtering method, as it not only has to preserve the AVO trend but somehow restore it. Although rank reduction is able to retrieve the general AVO character, it fails to fully restore the trend on the far offset. This is not only observed on the experimental results but also on the field data for which the far offset is frequently the nosiest receiver. This questions the validity of the AVO response on far offsets when land data are collected in noisy terrains. In such settings, the inversion of the density term from large angles reflections can be biased, adding uncertainty to the estimation of Young's Modulus and the brittleness index that involves the density term in their calculation.

Interpolating Seismic Data With Conditional Generative Adversarial Networks

Having dense and regularly sampled data is becoming increasingly important in seismic processing. However, due to physical or financial constraints, seismic data sets can be often undersampled. Occasionally, these data sets may also present bad or dead traces the geoscientist must deal with. Many works have tackled this problem using prestack data and can be classified in three main categories: wave-equation, domain transform, and prediction-error-filter methods. In this letter, we assess the performance of a conditional generative adversarial network for the interpolation problem in poststack seismic data sets. To the best of our knowledge, this is the first work to evaluate a deep learning approach in this context. Quantitative and qualitative evaluations of our experiments indicate that deep networks may present an interesting alternative to classical methods.

Nonlinear beamforming for enhancing prestack seismic data with a challenging near surface or overburden

Abstract Modern land seismic data acquisition is moving from sparse grids of large source/receiver arrays to denser grids of smaller arrays or point-source, point-receiver systems. Large arrays were designed to attenuate ground-roll and backscattered noise and to increase overall signal-to-noise ratio (SNR). An example of a typical raw common-shot gather acquired using a legacy acquisition design with 72 geophones in a group and five vibrators per sweep is shown in Figure 1b. We can clearly see that the ground-roll noise with low apparent velocity was partially attenuated by field arrays, and reflection events with high apparent velocity are strong and can be reliably identified. Decreasing the size of field arrays during acquisition in arid environments leads to dramatic decrease in data SNR. An example of raw common-shot gather from a 2D line acquired using a single-sensor survey is shown in Figure 1a. In contrast to legacy data, the single-sensor data is dominated by noise caused by severe multiple scattering in complex near-surface layers and shows no apparent evidence of reflection signal. The sources and receivers were spaced at 10 m intervals in this recent 2D single-sensor survey. This sampling involves much denser acquisition compared to the conventional data using intervals of 30 m or more. Theoretically, high-density seismic acquisition better samples the entire wavefield (signal and noise) and is expected to result in improved imaging. Achieving this in practice with huge amounts of low SNR data proves to be very challenging. Conventional time processing tools such as surface-consistent scaling, deconvolution, static corrections, require reliable prestack signal in the data. Their application to modern seismic datasets acquired with small arrays often leads to unreliable results because the derived operators are based on noise and not on the expected signal. To extract the maximum value from dense high-channel acquisition, we need to enhance signal in the prestack data. Fortunately, densely sampled data gives us more flexibility than grouping geophones directly in the field.

Inversion-Based 3-D Seismic Denoising for Exploring Spatial Edges and Spatio-Temporal Signal Redundancy

Seismic data are increasingly required to be high quality for the continuous improvement of the degree of exploration. From the viewpoint of inversion, the utilization of more information is an effective way to improve the signal-to-noise ratio of seismic data. In this letter, we adopt simultaneous sparsity constraints of the first-order differences of signals along the time direction and two spatial directions, described by minimizing the Cauchy function, as a combined constraint (or regularization) term imposed on the time-domain data misfit to propose an inversion-based 3-D seismic denoising method. In this way, the redundancies among time slices and seismic sections along two spatial directions are simultaneously considered, and the edges along the spatial directions can be preserved. Through analyzing the first-order derivative of the sum of the data misfit term and the designed combined regularization term (or the objective function), we derive that the relationship between data and desired signal samples in the range of the first-order neighborhood can be expressed as a linear system with seven data-dependent coefficients. Furthermore, it can be inferred that the sparsity constraints of signal differences along different dimensional directions of 3-D data have some complementary functions of noise reduction and signal preservation. We use a 3-D synthetic data set, a 3-D real poststack data set, and a 3-D real prestack data set to determine that the proposed method is an effective amplitude-preservation denoising tool with an acceptable computational cost.

Determination of gas reservoir(s) using AVO inversion within “XY” field offshore Niger Delta

The AVO inversion analysis of pre-stack seismic data of “XY” field located within the offshore, Niger Delta Basin has been considered in this research work with the aim of using AVO inversion to delineate gas reservoirs within the field. Three different inversion techniques are carried out in order to obtain accurate reservoir signature. The conventional AVO inversion analysis of the field do not identify the gas reservoirs clearly as there are other zones without gas with similar signatures, however, the cross-plots analysis of elastic parameters and the inverted volumes are used to delineate two gas reservoirs at 2000 and 2100 ms. Conclusively, the inversion techniques help to map the reservoir zone in a better way by increasing the vertical resolution. AVO inversion analysis as used in this research help for better understanding of the physical properties of the delineated reservoirs.

Regularized inversion of amplitude-versus-incidence angle (AVA) based on a piecewise-smooth model

Different from the stacked seismic data, pre-stack data includes abundant information about shear wave and density. Through inversing the shear wave and density information from the pre-stack data, we can determine oil-bearing properties from different incident angles. The state-of-the-art inversion methods obtain either low vertical resolution or lateral discontinuities. However, the practical reservoir generally has sharp discontinuities between different layers in vertically direction and is horizontally smooth. Towards obtaining the practical model, we present an inversion method based on the regularized amplitude-versus-incidence angle (AVA) data to estimate the piecewise-smooth model from pre-stack seismic data. This method considers subsurface stratum as a combination of two parts: a piecewise smooth part and a constant part. To fix the ill-posedness in the inversion, we adopt four terms to define the AVA inversion misfit function: the data misfit itself, a total variation regularization term acting as a sparsing operator for the piecewise constant part, a Tikhonov regularization term acting as a smoothing operator for the smooth part, and the last term to smoothly incorporate a priori information for constraining the magnitude of the estimated model. The proposed method not only can incorporate structure information and a priori model constraint, but also is able to derive into a convex objective function that can be easily minimized using iterative approach. Compared with inversion results of TV and Tikhonov regularization methods, the inverted P-wave velocity, S-wave velocity and density of the proposed method can better delineate the piecewise-smooth characteristic of strata.

Zero-offset sections with a deblurring filter in the time domain

The conventional common-midpoint stack is not equivalent to the zero-offset section due to the existence of velocity uncertainty. To obtain a zero-offset reflection section that preserves most reflections and diffractions, we have developed a velocity-independent workflow for reconstructing a high-quality zero-offset reflection section from prestack data with a deblurring filter. This workflow constructs a migration image volume by prestack time migration using a series of constant-velocity models. A deblurring filter for each constant-velocity model is applied to each time-migration image to get a deblurred image volume. To preserve all events in the image volume, each deblurred image panel is demigrated and then summed over the velocity axis. Compared with the workflow without a deblurring filter, the composite zero-offset reflection section has higher resolution and fewer migration artifacts. We evaluate applications of our method to synthetic and field data to validate its effectiveness.

Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities

Time-lapse surface seismic surveys have been widely used at carbon sequestration sites for site characterization, monitoring subsurface CO2 plume migration, and detecting potential CO2 leakage from a storage reservoir. Monitoring in the first permeable unit directly above the primary seal is important for early detection of CO2 leakage. Forward modeling of time-lapse seismic data can be used to assess the utility of the seismic method for CO2 leakage detection. We develop a workflow for forward modeling of time-lapse seismic data, including constructing seismic velocity models using flow simulation outputs, modeling of pre-stack and post-stack synthetic seismic data following seismic data processing sequence and analysis of processed synthetic time-lapse seismic data. We apply the forward modeling and analysis workflow to assessing the detectability and the earliest detection time of seismic monitoring using the hypothetical CO2 leakage scenarios for a model geologic storage site with realistic geology. We derive the detection thresholds using the simulated normalized root-mean-square (NRMS) differences for the no-leakage case at a range of signal-to-noise ratios, representing the background noise levels in seismic data. We then compare NRMS differences triggered by the CO2 leakage to the detection thresholds at each time step to quantify the detectability and the earliest detection time of seismic monitoring. We analyze the effects of the acquisition parameters and elastic parameters on the produced synthetic seismic data and earliest detection time. Our modeling results indicate that high signal-to-noise ratio is needed to detect the CO2 leakage at the model site. Minimizing the background noise in seismic data is crucial for improving the detectability of the seismic method. Increasing the shot density or increasing the dominant frequency of the source wavelet is likely to increase the possibility of the leakage detection and reduce the time needed for the detection. The elastic parameters used in the rock physics modeling have significant effects on the resultant seismic velocity models and synthetic seismic data, highlighting their critical roles in understanding and interpreting time-lapse seismic reflection data associated with CO2 monitoring, verification and accounting activities.

Global optimization strategies for implementing 3D common-reflection-surface stack using the very fast simulated annealing algorithm: Application to real land data

The 3D common-reflection-surface (CRS) stack operator depends on eight kinematic wavefield attributes that must be extracted from the prestack data. These attributes are obtained by an efficient optimization strategy based on the maximization of the coherence measure of the seismic reflection events included by the CRS stacking operator. The main application of these kinematic attributes is to simulate zero-offset stacked data; however, they can also be used for regularization of the prestack data, prestack migration, and velocity model determination. The initial implementations of the 3D CRS stack used grid-search techniques to determine the attributes in several steps with the drawback that accumulated errors can deteriorate the final result. In this work, the global optimization very fast simulated annealing algorithm is used to search for the kinematic attributes by applying three optimization strategies for implementing CRS stacking: (1) simultaneous global search of five kinematic attributes of the 3D common-diffraction-surface stacking operator, (2) two-step global optimization strategy to first search for three attributes and then five attributes of the CRS stacking operator, and (3) simultaneous global search of eight kinematic attributes of the CRS operator. The proposed CRS stacking algorithms are applied to land data of the Potiguar Basin, Brazil. It is demonstrated that the one-step optimization strategy of the eight parameters produces the best results, however, with a higher computational cost.

A comparative study of common-reflection-surface prestack time migration and data regularization: Application in crooked-line data

To improve the time-domain imaging of poor-quality seismic data, the common-reflection-surface (CRS) stack method was introduced to simulate zero-offset (ZO) stacked sections from a multicoverage data set based on automatic coherence analysis of seismic signals. This method produces improved ZO stacked sections with a high signal-to-noise ratio (S/N) and good continuity of reflection events. However, the stacking results may have some undesirable artifacts that can degrade the poststack migrated image. To overcome these drawbacks, I have developed a prestack data regularization method, based on CRS partial stacks, which produces prestack data with high S/N and enhanced reflection events. The regularized data are usually applied for velocity analysis and conventional prestack migration in the time and depth domains. Recently, the CRS stacking operator has also been applied for developing a new type of prestack beam migration. This new migration combines the classic Kirchhoff migration with the CRS stack method, in which the beam-forming process stacks locally coherent events that are performed using the CRS operator during migration. This work reviews this CRS-based prestack migration method in the time domain and presents a comparative study with the main standard applications of the CRS stack method, such as CRS stacking plus poststack time migration and CRS-based regularization plus prestack time migration (PSTM). To evaluate its effectiveness and reliability, CRS-based PSTM and CRS-based prestack data regularization were applied in a crooked line. The time-migrated image resulting from the regularized data has strong migration artifacts due to the crookedness of the seismic line; in contrast, the CRS-based time migration provides a good-quality image without migration artifacts.

Structurally oriented coherent noise filtering

All seismic data, whether 2D, 3D, post-stack or pre-stack, contains noise. Typically, this noise is comprised of both coherent and random components. Coherent noise presents itself as regular patterns in the seismic data. It may appear to be random or coherent depending on the orientation of the slice on which it is being observed. For example, coherent noise associated with acquisition may appear random on vertical slices through the volume, with its coherent nature becoming apparent on horizontal slices through the volume. When seismic data is processed, an effort is made to reduce the coherent noise in the seismic data by applying a variety of signal processing techniques. However, when the seismic volume is delivered to the client, it often includes remnant noise that processing was unable to remove without having a deleterious effect on the amplitudes and bandwidth of the seismic data. Since the processed seismic volume typically contains coherent noise, managing that noise within an interpretation system is critical, as it has a significant negative impact on semi-automatic and automatic interpretation workflows and techniques including autotracking of horizons, imaging of faults and fractures, automatic extraction of faults, and interpretation of stratigraphy and geomorphology. An obvious example is the effect of noise on edge attributes (i.e., ‘coherence’ class attributes). Any noise in the seismic data, whether random or coherent, will appear as edges in the seismic attribute volume and may obscure the geologic features of interest in the edge attribute volume (faults, fractures, stratigraphy, and geomorphology). Finally, many interpreters today must use older 3D seismic volumes, merged seismic volumes of a variety of acquisition designs and processing workflows. Even if the pre-stack data is still available, their company may not be in a position to spend the money required to re-process the data. Yet they still need and expect to be able to use modern advanced interpretation technology on the data. In response to this need a post-stack structurally oriented coherent noise filtering process has been developed and is described, with an application to 3D seismic surveys from the North Sea. The results are evaluated by comparing seismic volumes, edge attribute volumes, and amplitude and phase spectra pre- and post-filtering.

INTEGRATED AVO, ELASTIC SEISMIC INVERSION AND PETROPHYSICAL ANALYSIS FOR RESERVOIR CHARACTERIZATION: A CASE STUDY OF GAS FIELD, SOUTH SUMATERA BASIN

Integrated Amplitude Versus Offset ( AVO), elastic seismic inversion and petrophysical analysis have been successfully applied to estimate the elastic parameters of the reservoir for a case study of the gas field in south Sumatera basin. This paper aims to have better understanding the petrophysical properties of the reservoir. The petrophysical analysis was carried out by performing routine formation evaluation that includes calculation of shale volume, porosity, and water saturation of basic well log data. Sensitivity analysis was conducted to evaluate the sensitivity parameters of the log for changing in lithology, porosity, and fluid content in the reservoir. For completing the availability of elastic parameter from well log data, shear wave logs were derived from Castagna’s mudrock line relationship. Further, P-impedance, S-impedance, VpVs ratio, LambdaRho (λρ), MuRho (μρ) and density(ρ) were then calculated through a Lambda-Mu-Rho (LMR) transformation. Prior to performing AVO analysis and elastic seismic inversion, super gather technique was applied to improve the reliability of pre-stack seismic data. Elastic seismic inversion was carried out to extract the lateral elastic properties to capture lithology and fluid changes in the reservoir. In addition, AVO analysis of pre-stacked data was applied to identify hydrocarbon-bearing sandstone at target zone. The petrophysical analysis shows that porosity versus density crossplot is able to distinguish sand-shale based on 34% shale volume cutoff, while LMR crossplot is able to delineate hydrocarbon zone at water saturation value under 65%. The predicted lateral elastic parameter shows slightly higher value compare to overlying layer.

Application of simultaneous prestack inversion in reservoir facies identification

Poststack inversion is purely acoustic because there is no mode conversion at normal incidence. Therefore, P-wave impedance is the only information that can be estimated from poststack inversion of P-wave data. In full prestack inversion, both the low- and high-frequency components of the P-wave acoustic impedance can be extracted from the seismic data. In addition to the P-wave acoustic impedance, S-wave information or Poisson's ratio can also be estimated from prestack data. This provides fluid information for the reservoirs; thus, prestack inversion has an advantage over poststack inversion. We determined k, KC, m, and mC which are, respectively, the slope of the natural logarithm of shear impedance against the natural logarithm of acoustic impedance, the intercept of the natural logarithm of shear impedance against the natural logarithm of acoustic impedance, the slope of the natural logarithm of density against the natural logarithm of acoustic impedance, and the intercept of the natural logarithm of shear impedance against the natural logarithm of acoustic impedance. We also needed a set of angle-dependent wavelets, which are derived from angle stacks. Hence, we built three angle stacks; the near-angle stack (0–11 degrees), middle-angle stack (11–20 degrees), and far-angle stack (20–29 degrees). Using these angle stacks, we built three statistical angle-dependent wavelets. Finally, with log information we built an initial model for acoustic impedance, and tried to solve the inversion matrix using the conjugate gradient method. By solving this, we could simultaneously derive the acoustic impedance, shear impedance, and density sections from prestack data. Simultaneous inversion used the assumption of a constant shear wave to compressional wave velocity ratio, and the generalized Garnder equation, which are only valid in clastic rocks. The simultaneous prestack inversion method was used for the identification of reservoir facies in one of the hydrocarbon fields in Iran. With this method, four facies were identified; these were confirmed by reservoir geological information and well data.

Identification of morphologies of gas hydrate distribution based on amplitude variation with angle analysis

There are two basic distribution morphologies of gas hydrates in nature: pore filling and fracture filling. Identification of gas hydrate morphologies is essential for improved resource evaluation and exploitation. Improper exploitation may cause seafloor instability, lead to atmospheric venting, and exacerbate the greenhouse effect. To identify gas hydrate morphologies, we combine rock-physics modeling and amplitude variation with angle (AVA) analysis to study the theoretical AVA patterns of the bottom simulating reflector (BSR) beneath the pore-filling and fracture-filling gas hydrate bearing sediments (GHBS), respectively. The theoretical results indicate completely different AVA patterns between these two morphologies. The AVA of the BSR beneath the pore-filling GHBS shows a class III anomaly with negative intercept and gradient. However, the AVA of the BSR beneath the fracture-filling GHBS exhibits a class IV anomaly with a negative intercept and positive gradient. In addition, the theoretical AVA intercept and gradient are affected by the fracture orientation, manifesting an anisotropic signature varying with the fracture dip and azimuth. The processed prestack data at well sites 08 and 16 in the northern South China Sea are selected for testing our method. The AVA analysis of the actual BSRs beneath the pore-filling and fracture-filling GHBS at these sites shows class III and IV responses, respectively, in agreement with our theoretical results. The gas hydrate saturations are also estimated by comparing the theoretical AVA with the actual AVA patterns. The estimations at site 08 are close to those estimated from the pore-water freshening.

A two-step inversion approach for seismic-reservoir characterization and a comparison with a single-loop Markov-chain Monte Carlo algorithm

We have evaluated a two-step Bayesian algorithm for seismic-reservoir characterization, which, thanks to some simplifying assumptions, is computationally very efficient. The applicability and reliability of this method are assessed by comparison with a more sophisticated and computer-intensive Markov-chain Monte Carlo (MCMC) algorithm, which in a single loop directly estimates petrophysical properties and lithofluid facies from prestack data. The two-step method first combines a linear rock-physics model (RPM) with the analytical solution of a linearized amplitude versus angle (AVA) inversion, to directly estimate the petrophysical properties, and related uncertainties, from prestack data under the assumptions of a Gaussian prior model and weak elastic contrasts at the reflecting interface. In particular, we use an empirical, linear RPM, properly calibrated for the investigated area, to reparameterize the linear time-continuous P-wave reflectivity equation in terms of petrophysical contrasts instead of elastic constants. In the second step, a downward 1D Markov-chain prior model is used to infer the lithofluid classes from the outcomes of the first step. The single-loop (SL) MCMC algorithm uses a convolutional forward modeling based on the exact Zoeppritz equations, and it adopts a nonlinear RPM. Moreover, it assumes a more realistic Gaussian mixture distribution for the petrophysical properties. Both approaches are applied on an onshore 3D seismic data set for the characterization of a gas-bearing, clastic reservoir. Notwithstanding the differences in the forward-model parameterization, in the considered RPM, and in the assumed a priori probability density functions, the two methods yield maximum a posteriori solutions that are consistent with well-log data, although the Gaussian mixture assumption adopted by the SL method slightly improves the description of the multimodal behavior of the petrophysical parameters. However, in the considered reservoir, the main difference between the two approaches remains the very different computational times, the SL method being much more computationally intensive than the two-step approach.

Spark-based intelligent parameter inversion method for prestack seismic data

Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information. Rich reservoir parameter information can be obtained through inversion of prestack data. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is urgently needed to solve the inversion problem of prestack seismic big data. Since local optimum may be easily caught when genetic algorithm is used to optimize elastic parameters, the inversion effect is not obvious. In particular, the optimization effect for the density parameters is not good. An intelligent optimization algorithm is proposed in this paper for elastic parameter inversion of prestack seismic data. The algorithm improves genetic manipulation. The improved algorithm has been used for model trial for log data, and good inversion effect has been achieved. The inverted elastic parameters well fit with the log curve of the theoretical model. The improved algorithm effectively improves the inversion accuracy of density parameters. In this paper, the algorithm has been implemented on Spark model, and the results show that the parallel model can effectively reduce operation time of the algorithm.

A Hybrid Seismic Inversion Method for VP/VS Ratio and Its Application to Gas Identification

The ratio of compressional wave velocity to shear wave velocity (VP/VS ratio) has established itself as one of the most important parameters in identifying gas reservoirs. However, considering that seismic inversion process is highly non-linear and geological conditions encountered may be complex, a direct estimation of VP/VS ratio from pre-stack seismic data remains a challenging task. In this paper, we propose a hybrid seismic inversion method to estimate VP/VS ratio directly. In this method, post- and pre-stack inversions are combined in which the pre-stack inversion for VP/VS ratio is driven by the post-stack inversion results (i.e., VP and density). In particular, the VP/VS ratio is considered as a model parameter and is directly inverted from the pre-stack inversion based on the exact Zoeppritz equation. Moreover, anisotropic Markov random field is employed in order to regularise the inversion process as well as taking care of geological structures (boundaries) information. Aided by the proposed hybrid inversion strategy, the directional weighting coefficients incorporated in the anisotropic Markov random field neighbourhoods are quantitatively calculated by the anisotropic diffusion method. The synthetic test demonstrates the effectiveness of the proposed inversion method. In particular, given low quality of the pre-stack data and high heterogeneity of the target layers in the field data, the proposed inversion method reveals the detailed model of VP/VS ratio that can successfully identify the gas-bearing zones.

Application of supergrouping to enhance 3D prestack seismic data from a desert environment

Fit-for-purpose enhancement remains a critical task for processing land seismic data, especially with the increasing popularity of high-channel-count and single-sensor data. We describe here a flexible scheme called smart supergrouping that performs summation of traces from neighboring shots and receivers. Supergrouping enhances desired reflection signals while suppressing ground roll and other noise. It also delivers prestack data of significantly high quality, critical for deriving velocities, deconvolution operators, scalars, and statics, as well as for improved imaging. While similar in concept to field source/receiver array forming, supergrouping may be applied to data already acquired with field arrays. Unlike field arrays, supergrouped data have kinematic corrections applied with overlapping apertures. We also have the ability to compensate for intra-array statics and wavelet variations. We demonstrate the signal-enhancing abilities of such generalized supergroups with normal-moveout corrections on challenging land and ocean-bottom-cable seismic data from Saudi Arabia. Applications of supergrouped data cover various steps of land seismic processing from first-break picking to deconvolution to statics to full-waveform inversion and imaging.

Extending the useful angle range for elastic inversion through the amplitude-versus-angle full-waveform inversion method

We have developed the amplitude versus angle full-waveform inversion (AVA-FWI) method. This method considers the complete seismic response of the layered medium, and so it is capable of correctly handling seismic amplitudes from prestack data with a wide angle range. This capability is very important because a reliable estimate of the elastic parameters and the density requires an incidence angle that goes beyond 30°. Our method inputs seismic traces from prestack time-migrated gathers ordered by angle of incidence and works under the local 1D assumption. AVA-FWI is a nonlinear inversion based on forward modeling by the reflectivity method, which substantially increases its computational cost with respect to conventional AVA inversion. To address this problem, we developed an efficient routine for angle gather modeling and a new method for differential seismogram generation that greatly reduces the amount of computation involved in this task. The AVA-FWI method was applied to synthetic data and to a geophysical reservoir characterization case study using the North Viking Graben open data set.