Common Reflection Point Gathers Research Articles

Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas

The Arbuckle Group in southern Kansas has been investigated for carbon geosequestration-related studies. In this study, we evaluated seismic-scale petrophysically defined facies variability of the Arbuckle Group at the Wellington Field, Kansas, using quantitative seismic interpretation and a supervised Random Forest classification approach. We first defined three petrophysics-based rock types (petrofacies) from core-derived porosity and permeability measurements using the flow-zone indicator (FZI) approach. Then, using the artificial neural network (ANN), we classified these petrofacies in non-cored intervals. We observed that petrofacies 1 corresponds to medium and coarse-grained dolomitic packstone, wackestone, and dolomitic breccia with up to 8% porosity and Darcy-scale permeability values. Whereas petrofacies 2 and 3 correspond to argillaceous and fine-grained micritic dolomites and dolomitic mudstones with lower permeability values for a given porosity, with respect to petrofacies 1. Using the common reflection-point gathers, we performed pre-stack seismic inversion and calculated various amplitude-versus-offset (AVO) attribute volumes. We used these elastic properties and AVO attribute volumes as input for estimating supervised seismic-scale 3D petrofacies and petrofacies probability volumes using the Random Forest algorithm. Results reveal the complex distribution of petrofacies in the candidate injection and baffle zones in the study area, where petrofacies 1 is mainly prevalent within the lower and upper portions of the Arbuckle group, while petrofacies 2 and 3 are mainly present in the middle Arbuckle interval. The workflow we present through this study provides spatial variability of facies distribution that is reflective of actual lithology and petrophysical properties of the Arbuckle group in the study area with limited well control.

Seismic profile denoising based on common-reflection-point gathers using convolution neural networks

Abstract With the development of seismic surveys and the decline of shallow petroleum resources, high resolution and high signal-to-noise ratio have become more important in seismic processing. To improve the quality of seismic data, stationary-phase migration based on dip-angle gathers can be used to separate the reflected waves and noise. However, this method is very computationally intensive and heavily dependent on expert experience. Neural networks currently have powerful adaptive capabilities and great potential to replace artificial processing. Certain applications of convolution neural networks (CNNs) on stack profiles lead to a loss of amplitude information. Therefore, we have developed CNNs for noise reduction based on common-reflection-point (CRP) gathers. We used CRP gathers of stationary-phase migration as labels and CRP gathers of conventional prestack time migration as inputs. In addition, we analyzed the seismic amplitude properties and demonstrated the neural network optimization process and results. The results showed that our methods can achieve fast and reliable denoising and produce high-quality stack profiles that contain true amplitude information. Furthermore, the predicted high-quality CRP gathers can be used for further processing steps, such as normal moveout correction and amplitude variation with offset.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

Common-reflection-point (CRP) gather is one extensively used prestack seismic data type. However, CRP suffers more noise than a poststack seismic data set. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain, but also in the time-frequency (TF) domain. Therefore, we first use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the TF domain because the SSWT has better TF resolution and reconstruction properties. Then, we use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the TF domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples indicate that our method can effectively attenuate random noise with a better attenuation performance than the commonly used principal component analysis, [Formula: see text] filter, and the continuous wavelet transform method.

Seafloor elastic-parameter estimation based on a regularized AVO inversion method

Seafloor properties play important roles in both the civilian and the military domains. In this paper, we propose to use a regularization-based AVO (amplitude variation with offset) inversion method to estimate the elastic parameters of seafloor sediment along a seismic survey line. Conventional AVO research focuses mainly on the strata beneath seafloor sediment; accordingly, the AVO response of the interface between the seawater and seafloor sediment is typically ignored. Several studies on the estimation of seafloor elastic parameters have been published based on AVO theory. However, these earlier studies only consider one CRP (common reflection point) gather in the inversion process. When dealing with a long seismic survey with many CRPs, earlier methods involve clear random inversion errors along the survey line. To suppress the inversion errors, we try to regularize each of the elastic parameters along the seismic survey by a total-variation method. Synthetic tests demonstrate that the regularization-based method can effectively suppress the inversion errors along the seismic survey and yield much cleaner inversion results. Finally, we apply the regularization-based method to a real two-dimensional marine seismic survey and obtain satisfactory results, which further proves the efficacy of the method.

Automatic migration velocity estimation for prestack time migration

Migration velocity analysis is a labor-intensive part of the iterative prestack time migration (PSTM) process. We have developed a velocity estimation scheme to improve the efficiency of the velocity analysis process using an automatic approach. Our scheme is the numerical implementation of the conventional velocity analysis process based on residual moveout analysis. The key aspect of this scheme is the automatic event picking in the common-reflection point (CRP) gathers, which is implemented by semblance scanning trace by trace. With the picked traveltime curves, we estimate the velocities at discrete grids in the velocity model using the least-squares method, and build the final root-mean-square (rms) velocity model by spatial interpolation. The main advantage of our method is that it can generate an appropriate rms velocity model for PSTM in just a few iterations without manual manipulations. In addition, using the fitting curves of the picked events in a range of offsets to estimate the velocity model, which is fitting to a normal moveout correction, can prevent our scheme from the local minima issue. The Sigsbee2B model and a field data set are used to verify the feasibility of our scheme. High-quality velocity model and imaging results are obtained. Compared with the computational cost to generate the CRP gathers, the cost of our scheme can be neglected, and the quality of the initial velocity is not critical.

Common reflection point stacking for ocean-bottom node data

Due to the asymmetrical ray paths caused by the large elevation differences between source and receiver locations in ocean-bottom node (OBN) survey, conventional common midpoint (CMP) stacking is unsuitable for processing OBN data. To mitigate this problem, we propose a common reflection point (CRP) stacking method, which enables us to efficiently produce accurate CRP-stacked image and stacking velocity model for subsequent processing workflow. For each input trace, we estimate the samples representing the reflection points in the output time section using stacking velocity, and then use a modified double-square-root (DSR) formula to map the amplitudes of input trace to these samples, which are progressively accumulated to form moveout-corrected CRP gathers. We can save computational costs by restricting the samples that need to be processed based on OBN reflection geometry, and use conventional moveout analysis of the CRP gathers to iteratively update the stacking velocity model. With tests on synthetic and field-data examples, we demonstrate the advantages of the proposed CRP stacking over conventional CMP stacking.

Qualitative seismic attenuation parameter estimation based on prestack data in the continuous wavelet domain

Because the seismic wave propagates through the subsurface, part of the elastic energy eventually ends up as heat energy. This phenomenon is known as absorption (or anelastic attenuation). The factors causing anelastic attenuation include fluid movement and grain boundary friction. The seismic quality factor ([Formula: see text]) quantifies the anelastic attenuation and is commonly used in assisting reservoir characterization. However, current [Formula: see text]-estimation approaches are mainly implemented on a poststack seismic volume. The [Formula: see text]-estimation approaches applied to poststack seismic data assume that the seismic data are normal incident reflections, and they do not consider the effect of the travel path on seismic attenuation. In theory, the attenuation degree of the low-frequency component should differ from the attenuation degree of the high-frequency component for large-offset seismic data. We have developed a method to qualitatively estimate seismic attenuation in the prestack seismic domain. A continuous wavelet transform is used to extract the low- and high-frequency components for the common-reflection point gathers. The difference between the amplitude of the low-frequency component and the amplitude of the high-frequency component is used to measure the seismic attenuation factor. We have determined the effectiveness of our method by applying it to synthetic and real seismic data.

True amplitude extraction technology and its application in pre-stack seismic data

Common-reflection-point (CRP) gather is a bridge that connects seismic data and petrophysical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are important means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then suppresses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio (SNR).

3D common-reflection-point-based seismic migration velocity analysis

Three-dimensional prestack depth migration and depth residual picking in common-image gathers (CIGs) are the most time-consuming parts of 3D migration velocity analysis. Most migration-based velocity analysis algorithms need spatial coordinates of reflection points and CIG depth residuals at different offsets (or angles) to provide updated velocity information. We propose a new algorithm that can analyze 3D velocity quickly and accurately. Spatial coordinates and orientations of reflection points are provided by a 3D prestack parsimonious depth migration; the migration involves only the time samples picked from the salient reflection events on one 3D common-offset volume. Ray tracing from the reflection points to the surface provides a common-reflection-point (CRP) gather for each reflection point. Predicted (nonhyperbolic) moveouts for local velocity perturbations, based on maximizing the stacked amplitude, give the estimated velocity updates for each CRP gather. Then the velocity update for each voxel in the velocity model is obtained by averaging over all predicted velocity updates for that voxel. Prior model constraints may be used to stabilize velocity updating. Compared with other migration velocity analyses, the traveltime picking is limited to only one common-offset volume (and needs to be done only once); there is no need for intensive 3D prestack depth migration. Hence, the computation time is orders of magnitude less than other migration-based velocity analyses. A 3D synthetic data test shows the algorithm works effectively and efficiently.

CRP-based seismic migration velocity analysis

A new migration velocity analysis is developed by combining the speed of parsimonious prestack depth migration with velocity adjustments estimated within and across common-reflection-point (CRP) gathers. The proposed approach is much more efficient than conventional tomographic velocity analysis because only the traces that contribute to a series of CRP gathers are depth migrated at each iteration. The local interval-velocity adjustments for each CRP are obtained by maximizing the stack amplitude over the predicted (nonhyperbolic) moveout in each CRP gather; this does not involve retracing rays. At every iteration, the velocity in each pixel is updated by averaging over all the predicted velocity updates. Finally, CRP positions and orientations are updated by parsimonious migration, and rays are retraced to define new CRP gathers for the next iteration; this ensures internal consistency between the updated velocity model and the CRP gather. Because the algorithm has a gridded-model parameterization, no explicit representation or fitting of reflectors is involved. Strong lateral-velocity variations, such as those found at salt flanks, can be handled. Application to synthetic and field data sets show that the proposed algorithm works effectively and efficiently.

Fast model-based migration velocity analysis and reflector shape estimation

Migration velocity analysis can be made more efficient by preselecting the traces that contribute to a series of common-reflection-point (CRP) gathers and migrating only those traces. The data traces that contribute to a CRP for one reflection point on one layer are defined in a two-step procedure. First, poststack parsimonious Kirchhoff depth migration of zero-offset (or stacked) traces defines approximate reflector positions and orientations. Then, ray tracing from the reflection points for nonzero reflection angles defines the source and receiver locations of the data traces in the CRP gather. These traces are then prestack depth migrated, and the interval velocity model adjustment is obtained by fitting the velocity that maximizes the stack amplitude over the predicted (nonhyperbolic) moveout. A small number (2–3) of iterations converge to a 2D model of layer shape and interval velocity. Further efficiency is obtained by implementing layer stripping. The computation time is greatly reduced by combining parsimonious migration with processing only the salient portions of the whole seismic data set. The algorithm can handle lateral velocity variation within each layer as well as constant velocity. The computation time of the proposed algorithm is of the same order as that of the standard rms velocity scan method, but it does not have the inherent assumptions of the velocity scan method and is faster than current iterative prestack depth migration velocity analysis methods for typical field data.

Prestack migration concepts for AVO measurements on horizontal and dipping layers

Amplitude versus offset (AVO) processing is typically based on a model of horizontal reflectors and common midpoint (CMP) gathers. Seismic prestack migration on constant offset sections has also been used to improve the signal to noise ratio (SNR) of the AVO effect on the migrated CMP gathers that are referred to as common reflection point (CRP) gathers. The use of prestack migration in AVO measurements is aided by the scatterpoint concept that assumes reflectors can be composed of many scatterpoints (or reflecting elements) that are aligned along the reflector. The energy scattered by each point reconstructs to match the specula reflected energy. The energy from each scatterpoint forms a surface in the prestack volume that is often referred to as Cheops pyramid. The location of specula energy from a scatterpoint is identified by the tangential area between the reflected specula energy and the surface of Cheops pyramid. In addition, the incident and reflection angles from a scatterpoint may be superimposed on Cheops pyramid to identify any smearing that may occur. These principles apply to both horizontal and dipping layering. Conventional constant offset prestack migration can map horizontal and dipping specula energy to corresponding CRP gathers for AVO analysis. This method, however, requires an accurate velocity model before AVO measurements can be obtained. The equivalent offset method (EOM) of prestack migration forms prestack migration gathers that are referred to as common scatterpoint (CSP) gathers with no time shifting of the input data. This process uses minimal velocity information to rapidly form CSP gathers that that are also well suited for AVO analysis. After the gathers are formed, accurate velocities can be estimated and AVO measurements made.

Manual seismic reflection tomography

Prestack depth migration needs a good velocity model to produce a good image; in fact, finding the velocity model is one of the goals of prestack depth migration. Migration velocity analysis uses information produced by the migration to update the current velocity model for use in the next migration iteration. Several techniques are currently used to estimate migration velocities, ranging from trial and error to automatic methods like reflection tomography. Here, we present a method that combines aspects of some of the more accurate methods into an interactive procedure for viewing the effects of residual normal moveout corrections on migrated common reflection point (CRP) gathers. The residual corrections are performed by computing traveltimes along raypaths through both the current velocity model and the velocity model plus suggested model perturbations. The differences between those sets of traveltimes are related to differences in depth, allowing the user to preview the approximate effects of a velocity change on the CRP gathers without remigrating the data. As with automatic tomography, the computed depth differences are essentially backprojected along raypaths through the model, yielding a velocity update that flattens the gathers. Unlike automatic tomography, in which an algebraic inverse problem is solved by the computer for all geologic layers simultaneously, our method estimates shallow velocities before proceeding deeper and requires substantial user intervention, both in flattening individual CRP gathers and in deciding the appropriateness of the suggested velocity updates in individual geologic units.

Quantitative controls on 3-D seismic processing for prestack attribute analysis

Accurate prestack analysis relies on good‐quality seismic data. The seismic data volume should be organized into gathers of traces that sample the same subsurface region from a range of incident angles, with the change in reflected waveform across these traces being dependent on the rock properties and fluids at the reflecting boundary. Further, the signal to noise (S/N) ratio should be high enough so that the change in waveform and any anomalous behavior are accurately detectable. The aim of the processing scheme is to produce data in such a format without introducing artifacts. The main factors affecting prestack data quality can therefore be broken down into S/N ratio, imaging to common reflection point gathers, lateral resolution, and vertical resolution.