Interval velocity analysis using multiparameter common image gathers

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. U63-U72 ◽  
Author(s):  
Raanan Dafni ◽  
Moshe Reshef
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Structural Characteristics ◽  
Real Data ◽  
Image Point ◽  
Velocity Analysis ◽  
Depth Migration ◽  
Interval Velocity ◽  
Correctness Criterion ◽  
Stripping Method

We developed a nonconventional approach to interval velocity analysis. The motivation for this approach is based on the argument that when the subsurface structure is complex, velocity error cannot be related to a single parameter. The suggested analysis uses multiparameter common image gathers (MPCIGs), generated by standard prestack depth migration. The parameterization of these multiparameter gathers is directly related to the structural characteristics of the subsurface image points. The undesirable summation, which is usually involved in the generation of conventional common image gathers, is avoided. During the velocity analysis procedure, depth slices taken out of the calculated MPCIGs are examined. Each depth slice contains all seismic data that were migrated into a single image point associated with the specific depth slice. When the MPCIGs are generated with the correct velocity function, each depth slice holds all structural information associated with the corresponding image point. Through detailed analysis of 2D synthetic and real data examples, the influence of migration velocity errors on the accuracy of the migrated multiparameter gathers is demonstrated. A Kirchhoff-based algorithm is used for the migration along with a layer-stripping method, relying on velocity scans, for the analysis. A velocity correctness criterion was also verified, along with some suggestions on the practical usage of the method.

MAZ depth-velocity modelling and imaging with azimuthal anisotropy

2010 ◽  
Vol 50 (2) ◽  
pp. 723
Author(s):  
Sergey Birdus ◽  
Erika Angerer ◽  
Iftikhar Abassi
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Azimuthal Velocity ◽  
Velocity Model ◽  
Azimuthal Anisotropy ◽  
Lessons Learned ◽  
Seismic Survey ◽  
Depth Migration ◽  
Velocity Anisotropy ◽  
Interval Velocity

Processing of multi and wide-azimuth seismic data faces some new challenges, and one of them is depth-velocity modelling and imaging with azimuthal velocity anisotropy. Analysis of multi-azimuth data very often reveals noticeable fluctuations in moveout between different acquisition directions. They can be caused by several factors: real azimuthal interval velocity anisotropy associated with quasi-vertical fractures or present day stress field within the sediments; short-wavelength velocity heterogeneities in the overburden; TTI (or VTI) anisotropy in the overburden; or, random distortions due to noise, multiples, irregularities in the acquisition geometry, etcetera. In order to build a velocity model for multi-azimuth pre-stack depth migration (MAZ PSDM) taking into account observed azimuthal anisotropy, we need to recognise, separate and estimate all the effects listed above during iterative depth-velocity modelling. Analysis of seismic data from a full azimuth 3D seismic land survey revealed the presence of strong spatially variable azimuthal velocity anisotropy that had to be taken into consideration. Using real data examples we discuss major steps in depth processing workflow that took such anisotropy into account: residual moveout estimation in azimuth sectors; separation of different effects causing apparent azimuthal anisotropy (see A–D above); iterative depth-velocity modelling with azimuthal anisotropy; and, subsequent MAZ anisotropic PSDM. The presented workflow solved problems with azimuthal anisotropy in our multi-azimuth dataset. Some of the lessons learned during this MAZ project are relevant to every standard narrow azimuth seismic survey recorded in complex geological settings.

2-D continuation operators and their applications

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1846-1858 ◽  
Author(s):  
Claudio Bagaini ◽  
Umberto Spagnolini
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Integral Form ◽  
Amplitude Distribution ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Seismic Data Processing ◽  
Analysis Method ◽  
Standard Tool ◽  
Zero Offset

Continuation to zero offset [better known as dip moveout (DMO)] is a standard tool for seismic data processing. In this paper, the concept of DMO is extended by introducing a set of operators: the continuation operators. These operators, which are implemented in integral form with a defined amplitude distribution, perform the mapping between common shot or common offset gathers for a given velocity model. The application of the shot continuation operator for dip‐independent velocity analysis allows a direct implementation in the acquisition domain by exploiting the comparison between real data and data continued in the shot domain. Shot and offset continuation allow the restoration of missing shot or missing offset by using a velocity model provided by common shot velocity analysis or another dip‐independent velocity analysis method.

Wave-equation migration velocity analysis by focusing diffractions and reflections

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. U19-U27 ◽  
Author(s):  
Paul C. Sava ◽  
Biondo Biondi ◽  
John Etgen
Keyword(s):  
Wave Equation ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Depth Migration ◽  
Interval Velocity ◽  
Migration Velocity Analysis ◽  
Inversion Procedure ◽  
Velocity Information ◽  
Zero Offset

We propose a method for estimating interval velocity using the kinematic information in defocused diffractions and reflections. We extract velocity information from defocused migrated events by analyzing their residual focusing in physical space (depth and midpoint) using prestack residual migration. The results of this residual-focusing analysis are fed to a linearized inversion procedure that produces interval velocity updates. Our inversion procedure uses a wavefield-continuation operator linking perturbations of interval velocities to perturbations of migrated images, based on the principles of wave-equation migration velocity analysis introduced in recent years. We measure the accuracy of the migration velocity using a diffraction-focusing criterion instead of the criterion of flatness of migrated common-image gathers that is commonly used in migration velocity analysis. This new criterion enables us to extract velocity information from events that would be challenging to use with conventional velocity analysis methods; thus, our method is a powerful complement to those conventional techniques. We demonstrate the effectiveness of the proposed methodology using two examples. In the first example, we estimate interval velocity above a rugose salt top interface by using only the information contained in defocused diffracted and reflected events present in zero-offset data. By comparing the results of full prestack depth migration before and after the velocity updating, we confirm that our analysis of the diffracted events improves the velocity model. In the second example, we estimate the migration velocity function for a 2D, zero-offset, ground-penetrating radar data set. Depth migration after the velocity estimation improves the continuity of reflectors while focusing the diffracted energy.

Influence of structural dip angles on interval velocity analysis

Geophysics ◽  
2008 ◽  
Vol 73 (4) ◽  
pp. U13-U18 ◽  
Author(s):  
Moshe Reshef ◽  
Andreas Rüger
Keyword(s):  
Velocity Analysis ◽  
Prestack Depth Migration ◽  
Scattering Angle ◽  
Depth Migration ◽  
Interval Velocity ◽  
Important Input ◽  
The Common ◽  
Summation Process

Common scattering-angle and traditional common-offset gathers can be of limited use for interval velocity analysis in regions with complex geologic structures. In the summation process, which occurs when generating each trace in the common-image gather, vital information about structural dip is lost during prestack depth migration. This inadvertently lost data can provide important input to moveout-based velocity-updating algorithms. Maintaining this crucial dip information can improve the quality of the velocity analysis and imaging processes.

Case Study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Guofeng Liu ◽  
Xiaohong Meng ◽  
Johanes Gedo Sea
Keyword(s):  
Wave Equation ◽  
Image Quality ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Mineral Exploration ◽  
Velocity Model ◽  
Image Point ◽  
Single Shot ◽  
Data Set ◽  
Depth Migration

Seismic reflection is a proven and effective method commonly used during the exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we employed the attribute migration method. This worked by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponded to each image point was obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function was proposed to map every image point to a certain range of offsets. This helped improve the signal-to-noise ratio, which was especially important in handing low quality seismic data obtained during mineral exploration. Residual velocity analysis was applied to these gathers to update the velocity model and improve image quality. The offset-domain common-image gathers were also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, the proposed procedure achieved an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

Unified imaging of multichannel seismic data: Combining traveltime inversion and prestack depth migration

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE269-VE280 ◽  
Author(s):  
Priyank Jaiswal ◽  
Colin A. Zelt
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Velocity Model ◽  
Depth Image ◽  
Prestack Depth Migration ◽  
Traveltime Inversion ◽  
Depth Migration ◽  
Interval Velocity ◽  
Wide Aperture ◽  
Zero Offset

Imaging 2D multichannel land seismic data can be accomplished effectively by a combination of traveltime inversion and prestack depth migration (PSDM), referred to as unified imaging. Unified imaging begins by inverting the direct-arrival times to estimate a velocity model that is used in static corrections and stacking velocity analysis. The interval velocity model (from stacking velocities) is used for PSDM. The stacked data and the PSDM image are interpreted for common horizons, and the corresponding wide-aperture reflections are identified in the shot gathers. Using the interval velocity model, the stack interpretations are inverted as zero-offset reflections to constrain the corresponding interfaces in depth; the interval velocity model remains stationary. We define a coefficient of congruence [Formula: see text] that measures the discrepancy between horizons from the PSDM image andtheir counterparts from the zero-offset inversion. A value of unity for [Formula: see text] implies that the interpreted and inverted horizons are consistent to within the interpretational uncertainties, and the unified imaging is said to have converged. For [Formula: see text] greater than unity, the interval velocity model and the horizon depths are updated by jointly inverting the direct arrivals with the zero-offset and wide-aperture reflections. The updated interval velocity model is used again for both PSDM and a zero-offset inversion. Interpretations of the new PSDM image are the updated horizon depths. The unified imaging is applied to seismic data from the Naga Thrust and Fold Belt in India. Wide-aperture and zero-offset data from three geologically significant horizons are used. Three runs of joint inversion and PSDM are required in a cyclic manner for [Formula: see text] to converge to unity. A joint interpretation of the final velocity model and depth image reveals the presence of a triangle zone that could be promising for exploration.

Applying the Horizon Based Tomography Method to Update Interval Velocity Model, Identify The Structure of Pre-Stack Depth Migration 3D and Estimate The Hydrocarbon Reserve In SBI Field of North West Java Basin

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Sudra Irawan ◽  
Sismanto Sismanto ◽  
Adang Sukmatiawan
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Velocity Model ◽  
Research Area ◽  
Seismic Data Processing ◽  
West Java ◽  
North West ◽  
Depth Migration ◽  
Interval Velocity ◽  
Tomography Method

Seismic data processing is one of the three stages in the seismic method that has an important role in the exploration of oil and gas. Without good data processing, it is impossible to get seismic image cross section for good interpretation. A research using seismic data processing was done to update the velocity model by horizon based tomography method in SBI Field, North West Java Basin. This method reduces error of seismic wave travel time through the analyzed horizon because the existence velocity of high lateral variation in research area. There are three parameters used to determine the accuracy of the resulting interval velocity model, namely, flat depth gathers, semblance residual moveout that coincides with the axis zero residual moveout, and the correspondence between image depth (horizon) with wells marker  (well seismic tie). Pre Stack Depth Migration (PSDM) form interval velocity model and updating using horizon-based tomography method gives better imaging of under-surfaced structure results than PSDM before using tomography. There are three faults found in the research area, two normal faults have southwest-northeast strike and the other has northwest-southeast strike. The thickness of reservoir in SBI field, North West Java Basin, is predicted between 71 to 175 meters and the hydrocarbon (oil) reserve is predicted about  with 22.6% porosity and 70.7% water saturation. 

3‐D imaging using higher order fast marching traveltimes

Geophysics ◽  
2002 ◽  
Vol 67 (2) ◽  
pp. 604-609 ◽  
Author(s):  
Alexander Mihai Popovici ◽  
James A. Sethian
Keyword(s):  
Real Data ◽  
Second Order ◽  
Data Sets ◽  
Velocity Analysis ◽  
Fast Marching ◽  
First Order ◽  
Interval Velocity ◽  
Migration Velocity Analysis ◽  
Imaging Results ◽  
The Common

Recently, fast marching methods (FMM) beyond first order have been developed for producing rapid solutions to the eikonal equation. In this paper, we present imaging results for 3‐D prestack Kirchhoff migration using traveltimes computed using the first‐order and second‐order FMM on several 3‐D prestack synthetic and real data sets. The second order traveltimes produce a much better image of the structure. Moreover, insufficiently sampled first order traveltimes can introduce consistent errors in the common reflection point gathers that affect velocity analysis. First‐order traveltimes tend to be smaller than analytic traveltimes, which in turn affects the migration velocity analysis, falsely indicating that the interval velocity was too low.

Pitfalls in seismic processing: An application of seismic modeling to investigate acquisition footprint

2016 ◽  
Vol 4 (2) ◽  
pp. SG1-SG9 ◽  
Author(s):  
Marcus P. Cahoj ◽  
Sumit Verma ◽  
Bryce Hutchinson ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Seismic Attributes ◽  
Velocity Analysis ◽  
Seismic Modeling ◽  
Seismic Processing ◽  
The Real ◽  
Considerable Research ◽  
Data Volume ◽  
Ground Roll

The term acquisition footprint is commonly used to define patterns in seismic time and horizon slices that are closely correlated to the acquisition geometry. Seismic attributes often exacerbate footprint artifacts and may pose pitfalls to the less experienced interpreter. Although removal of the acquisition footprint is the focus of considerable research, the sources of such artifact acquisition footprint are less commonly discussed or illustrated. Based on real data examples, we have hypothesized possible causes of footprint occurrence and created them through synthetic prestack modeling. Then, we processed these models using the same workflows used for the real data. Computation of geometric attributes from the migrated synthetics found the same footprint artifacts as the real data. These models showed that acquisition footprint could be caused by residual ground roll, inaccurate velocities, and far-offset migration stretch. With this understanding, we have examined the real seismic data volume and found that the key cause of acquisition footprint was inaccurate velocity analysis.

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