SEMBLANCE RESIDUAL MOVEOUT ANALYSIS TO FIND ERORRS AND UPDATING THE INTERVAL VELOCITY MODEL MODE IN THE HORIZON BASED TOMOGRAPHY METHOD

The tomography method requires an excellent initial velocity model. On the horizon based tomography, it will correct the travel time error of seismic waves along the horizon which is analysed using input results from the analysis of residual depth moveout. In this study, a semblance residual moveout analysis will be conducted after the interval velocity model has applied to the SBI field seismic data (CDP Gathers and RMS velocity). Based on the imaging results generated by the PSDM running process, an aperture value of 550 for inline and 800 for crossline is selected. PSDM generated from the initial interval velocity model has an acoustic impedance value between 1000 kg/m2s to 14339.2 kg/m2s. The PSDM process, residual moveout analysis, and horizon-based tomography are carried out iteratively until the error in the interval velocity model approaches zero. In this study, five iterations were performed. The resulting residual moveout is increasingly oscillating around zero after the 5th iteration, which indicates that the error in the interval velocity model is getting smaller. There are two types of residual moveout, namely residuals moveout positively and residuals moveout negatively. Residual moveout positive indicates that the velocity used is too high, while the residual moveout negative indicates that the velocity used is too low. The identification of interval velocity model errors with analysis of residual moveout semblance is calculated from depth gathers. The semblance residual moveout analysis is used for the Pre Stack Depth Migration (PSDM) depth image analysis stage along with the marker (well data). .

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Method Comparison of Model Based Tomography and Grid Based Tomography to refine interval velocity

INDONESIAN JOURNAL OF APPLIED PHYSICS ◽

10.13057/ijap.v4i01.1170 ◽

2016 ◽

Vol 4 (01) ◽

pp. 63

Author(s):

Yuninggar Dwi Nugroho ◽

Sudarmaji S

Keyword(s):

Method Comparison ◽

Velocity Model ◽

Seismic Section ◽

Initial Interval ◽

Depth Migration ◽

Interval Velocity ◽

Model Based ◽

Time Migration ◽

Tomography Method ◽

Grid Based

The input data for pre stack time migration and pre stack depth migration is velocity model. The exact velocity model can provide maximum result in seismic section. The best seismic section can minimize possibility of errors during interpretation. Model based and grid based tomography are used to refine the interval velocity model. The interval velocity will be used as input in the pre stack depth migration. Initial interval velocity is obtained from RMS velocity using Dix formula. This velocity will be refined by global depth tomography method. The global depth tomography method is divided into model based and grid based tomography. Velocity analysis is performed along the horizon (depth model). Residual depth move out is obtained from picking velocity. It is used as input in tomography method. The flat gather is obtained at tenth iteration. The interval velocity that is obtained from tenth iteration has the small errors. Tomography method can provide maximum result on velocity refinement. That is shown by the result that the pre stack depth migration is much better than using initial interval velocity. The pull up effect can be corrected by tomography method.

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

Geophysics ◽

10.1190/1.2957761 ◽

2008 ◽

Vol 73 (5) ◽

pp. VE269-VE280 ◽

Cited By ~ 7

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.

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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

Jurnal Teknologi ◽

10.11113/jt.v69.3240 ◽

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.

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QUALITY IMPROVEMENT OF SEISMIC IMAGE FROM 2D PSDM (PRE STACK DEPTH MIGRATION) USING TOMOGRAPHY FOR INTERVAL VELOCITY MODEL REFINEMENT

BULLETIN OF THE MARINE GEOLOGY ◽

10.32693/bomg.28.2.2013.59 ◽

2016 ◽

Vol 28 (2) ◽

pp. 43

Author(s):

Hagayudha Timotius ◽

Yulinar Firdaus

Keyword(s):

Velocity Model ◽

Seismic Exploration ◽

Lateral Velocity ◽

Model Refinement ◽

Initial Interval ◽

Depth Migration ◽

Interval Velocity ◽

Imaging Tool ◽

Main Challenge ◽

Seismic Image

The main goal of seismic exploration is to get an accurate image of subsurface section so it can be easily interpreted. Pre Stack Depth Migration (PSDM) is such a powerful imaging tool especially for complex area such an area where strong lateral velocity variations exist. The main challenge of PSDM is the need of accurate interval velocity model.In this research, Dix Transformation, coherency inversion, and tomography are used for initial interval velocity model, and then tomography is used for interval velocity model refinement. We compare also between seismic image resulted from PSDM and PSTM to determine the best method. The seismic data that processed in this paper is derived from north western part of Australian Waters. Kata kunci: Pre Stack Depth Migration, Dix Transformation, coherency inversion, tomography. Tujuan utama dari eksplorasi seismik adalah menghasilkan citra yang akurat dari penampang bawah permukaan sehingga diinterpretasi lebih mudah. Pre Stack Depth Migration (PSDM) merupakan suatu metode yang memberikan hasil peningkatan kualitas citra seismik pada daerah kompleks dimana terjadi variasi kecepatan lateral yang signifikan. Salah satu syarat penting yang harus dipenuhi agar hasil PSDM lebih optimal adalah model kecepatan interval yang akurat. Dalam penelitian ini Transformasi Dix, inversi koheren, dan tomografi digunakan untuk memenuhi syarat tersebut. Perbandingan hasil penampang seimik PSDM dan PSTM dilakukan untuk menentukan metode terbaik. Data seismik yang diolah dalam tulisan ini berasal dari wilayah Perairan Baratlaut Australia. Kata kunci: Pre Stack Depth Migration, Transformasi Dix, inversi koheren, tomografi

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Migration of seismic data from inhomogeneous media

Geophysics ◽

10.1190/1.1441213 ◽

1981 ◽

Vol 46 (5) ◽

pp. 751-767 ◽

Cited By ~ 25

Author(s):

Les Hatton ◽

Ken Larner ◽

Bruce S. Gibson

Keyword(s):

Theoretical Approach ◽

Synthetic Data ◽

Velocity Model ◽

Implicit Assumption ◽

Geologic Structure ◽

Structural Shape ◽

Depth Migration ◽

Position Errors ◽

Time Migration ◽

Imaging Results

Because conventional time‐migration algorithms are founded on the implicit assumption of locally lateral homogeneity, they leave events mispositioned when overburden velocity varies laterally. The ray‐theoretical depth migration procedure of Hubral often can provide adequate first‐order corrections for such position errors. Complex geologic structure, however, can so severely distort wavefronts that resulting time‐migrated sections may be barely interpretable and thus not readily correctable. A more accurate, wave‐theoretical approach to depth migration then becomes essential to image the subsurface properly. This approach, which transforms an unmigrated time section directly into migrated depth, more completely honors the wave equation for a medium in which variations in interval velocity and details of structural shape govern wave propagation. Where geologic structure is complicated, however, we usually lack an accurate velocity model. It is important, therefore, to understand the sensitivity of depth migration to velocity errors and, in particular, to assess whether it is justified to go to the added effort of doing depth migration. We show a synthetic data example in which the wave‐theoretical approach to depth migration properly images deep reflections that are poorly resolved and left distorted by either time migration or ray‐theoretical depth migration. These imaging results are, moreover, surprisingly insensitive to errors introduced into the velocity model. Application to one field data example demonstrates the superior treatment of amplitude and waveform by wave‐theoretical depth migration. In a second data example, deep reflections are so influenced by anomalous overburden structure that the only valid alternative to performing wave‐theoretical depth migration is simply to convert the unmigrated data to depth. When the overburden is laterally variable, conventional time migration of unstacked data can be as destructive to steeply dipping reflections as is CDP stacking prior to migration. A schematic example illustrates that when migration of unstacked data is judged necessary, it should normally be performed as a depth migration.

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3D PRE-STACK DEPTH MIGRATION: A TOOL FOR REDUCING EXPLORATION RISK IN THE SWAN GRABEN, TIMOR SEA

The APPEA Journal ◽

10.1071/aj04033 ◽

2005 ◽

Vol 45 (1) ◽

pp. 421

Author(s):

P. Bocca ◽

L. Fava ◽

E. Stolf

Keyword(s):

Seismic Data ◽

Integrated Approach ◽

Velocity Model ◽

Fault Reactivation ◽

Depth Image ◽

Substantial Contribution ◽

Surface Geometry ◽

Depth Migration ◽

Imaging Tool ◽

Seismic Image

3D pre-stack depth migration (PSDM) reprocessing was conducted in 2003 on a portion of the Onnia 3D seismic cube, located in exploration permit AC/P-21, Timor Sea.The main objective of the reprocessing was to obtain the best seismic depth image and the most realistic structural reconstruction of the sub-surface to mitigate the risk factors associated with trap definition (trap retention and trap efficiency). This represents one of the main challenges for oil exploration in the area.The 3D PSDM methodology was chosen as the most appropriate imaging tool to define the correct sub-surface geometry and fault imaging through the use of an appropriate velocity field. An integrated approach to building the final velocity model was adopted, with a substantial contribution from the regional geological model.Several examples are given to demonstrate that the 3D PSDM reprocessing significantly improved the seismic image and thus the confidence in the interpretation, contributing strongly to the definition of the exploration targets.The interpretation of the new seismic data has resulted in a new structural picture in which higher confidence in seismic imaging has improved fault correlation. This has enabled better structural definition at the Middle Jurassic Plover Formation level that has reduced the complexity of the large Vesta Prospect, in the centre of the Swan Graben to the northwest of East Swan–1. Improved understanding of the fault reactivation mechanism and the structural elements of the trap (trap integrity) were eventually incorporated in the prospect risking.In the Swan Graben 3D PSDM has proved to be a very powerful instrument capable of producing significant impact on the exploration even in an area with a complex geological setting and a fairly poor seismic data quality.

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Incorporating geologic information into reflection tomography

Geophysics ◽

10.1190/1.1707073 ◽

2004 ◽

Vol 69 (2) ◽

pp. 533-546 ◽

Cited By ~ 98

Author(s):

Robert G. Clapp ◽

Biondo L. Biondi ◽

Jon F. Claerbout

Keyword(s):

Velocity Structure ◽

Null Space ◽

Velocity Model ◽

Prestack Depth Migration ◽

Depth Migration ◽

Interval Velocity ◽

Velocity Models ◽

Regularized Inversion ◽

Symmetric Operators ◽

Reflection Tomography

In areas of complex geology, prestack depth migration is often necessary if we are to produce an accurate image of the subsurface. Prestack depth migration requires an accurate interval velocity model. With few exceptions, the subsurface velocities are not known beforehand and should be estimated. When the velocity structure is complex, with significant lateral variations, reflection‐tomography methods are often an effective tool for improving the velocity estimate. Unfortunately, reflection tomography often converges slowly, to a model that is geologically unreasonable, or it does not converge at all. The large null space of reflection‐tomography problems often forces us to add a sparse parameterization of the model and/or regularization criteria to the estimation. Standard tomography schemes tend to create isotropic features in velocity models that are inconsistent with geology. These isotropic features result, in large part, from using symmetric regularization operators or from choosing a poor model parameterization. If we replace the symmetric operators with nonstationary operators that tend to spread information along structural dips, the tomography will produce velocity models that are geologically more reasonable. In addition, by forming the operators in helical 1D space and performing polynomial division, we apply the inverse of these space‐varying anisotropic operators. The inverse operators can be used as a preconditioner to a standard tomography problem, thereby significantly improving the speed of convergence compared with the typical regularized inversion problem. Results from 2D synthetic and 2D field data are shown. In each case, the velocity obtained improves the focusing of the migrated image.

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Automatic event picking in prestack migrated gathers using a probabilistic neural network

Geophysics ◽

10.1190/1.1487094 ◽

2001 ◽

Vol 66 (5) ◽

pp. 1488-1496 ◽

Cited By ~ 10

Author(s):

Michael E. Glinsky ◽

Grace A. Clark ◽

Peter K. Z. Cheng ◽

K. R. Sandhya Devi ◽

James H. Robinson ◽

...

Keyword(s):

Neural Network ◽

Probabilistic Neural Network ◽

Ground Truth ◽

Velocity Model ◽

Depth Image ◽

Model Inversion ◽

Depth Migration ◽

Signal Image ◽

Event Times ◽

Selection Algorithms

We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic moveout. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event‐picking algorithm has the potential to reduce significantly the cycle time and cost of 3‐D prestack depth migration while making the velocity model inversion more robust.

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Hybrid migration: A cost‐effective 3-D depth‐imaging technique

Geophysics ◽

10.1190/1.1444166 ◽

1997 ◽

Vol 62 (2) ◽

pp. 568-576 ◽

Cited By ~ 7

Author(s):

Young C. Kim ◽

Worth B. Hurt, ◽

Louis J. Maher ◽

Patrick J. Starich

Keyword(s):

Model Building ◽

Cost Effective ◽

Velocity Model ◽

Depth Image ◽

Hybrid Technique ◽

Prestack Depth Migration ◽

Depth Imaging ◽

Depth Migration ◽

Time Migration ◽

Prestack Time Migration

The transformation of surface seismic data into a subsurface image can be separated into two components—focusing and positioning. Focusing is associated with ensuring the data from different offsets are contributing constructively to the same event. Positioning involves the transformation of the focused events into a depth image consistent with a given velocity model. In prestack depth migration, both of these operations are achieved simultaneously; however, for 3-D data, the cost is significant. Prestack time migration is much more economical and focuses events well even in the presence of moderate velocity variations, but suffers from mispositioning problems. Hybrid migration is a cost‐effective depth‐imaging approach that uses prestack time migration for focusing; inverse migration for the removal of positioning errors; and poststack depth migration for proper positioning. When lateral velocity changes are moderate, the hybrid technique can generate a depth image that is consistent with a velocity field. For very complex structures that require prestack depth migration, the results of the hybrid technique can be used to create a starting velocity model, thereby reducing the number of iterations for velocity model building.

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Velocity analysis without picking

Geophysics ◽

10.1190/1.1442643 ◽

1989 ◽

Vol 54 (2) ◽

pp. 191-199 ◽

Cited By ~ 59

Author(s):

John L. Toldi

Keyword(s):

Field Data ◽

Velocity Model ◽

Point Of View ◽

Velocity Analysis ◽

Lateral Variation ◽

Data Set ◽

Initial Interval ◽

Analysis Algorithm ◽

Interval Velocity ◽

Interval Velocities

Conventionally, interval velocities are derived from picked stacking velocities. The velocity‐analysis algorithm proposed in this paper is also based on stacking velocities; however, it eliminates the conventional picking stage by always considering stacking velocities from the point of view of an interval‐velocity model. This view leads to a model‐based, automatic velocity‐analysis algorithm. The algorithm seeks to find an interval‐velocity model such that the stacking velocities calculated from that model give the most powerful stack. An additional penalty is incurred for models that differ in smoothness from an initial interval‐velocity model. The search for the best model is conducted by means of a conjugate‐gradient method. The connection between the interval‐velocity model and the stacking velocities plays an important role in the algorithm proposed in this paper. In the simplest case, stacking velocity is assumed to be equal to rms velocity. For the more general case, a linear theory is developed, connecting interval velocity and stacking velocity through the intermediary of traveltime. When applied to a field data set, the method produces an interval‐velocity model that explains the lateral variation in both stacking velocity and traveltime.

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