The Puffin Field ? a geophysical enigma

The Puffin Field is located within the Vulcan Sub-basin of the Timor sea, off the Northwest Coast of Australia. It lies within the offshore exploration permit AC/P2, operated by BHP Petroleum and its co-venturers. It is situated on the Ashmore Platform, an old Triassic horst which is normal faulted against the Swan Graben, a major Mesozoic depocentre and the regional source area. Three wells were drilled in the 1970's. Puffin-1 and Puffin-3 encountered oil in "FIT" tests from within the Maastrichtian 100 ft sand, and Puffin-2 flowed over 4000 barrels of oil per day from a slightly younger 4 m sand. On examination of the results of the Puffin wells, it was evident that there were severe velocity anomalies and differing oil water contacts in the Puffin field. The top of the 100 ft reservoir sand is at 2031.4 m subsea in Puffin-1, 2045 m subsea at Puffin-2 and 2074 m subsea at Puffin-3. The two way times to these events were 1392 ms, 1328 ms and 1398 ms respectively. The interpreted oil water contacts in Puffin-1 and Puffin-3 were 2033 and 2077 ms subsea respectively with no contact seen at Puffin-2. In an attempt to resolve these anomalies the AC/P2 joint venture undertook a detailed seismic reprocessing project of the 1980 data with special emphasis on detailed velocity analysis. This 1987 reprocessing effort involved two passes of velocity filtering and velocity analysis at every 600 m. Velocity analyses were picked on a horizon-consistent basis, such that variations in interval velocity for key horizons could be established for later use in depth conversion. Although sceptical in using stacking functions as the input velocities to depth conversion, they were used, as no viable alternative was feasible. Data quality was reliable to the top of the Palaeocene Calcilutite, and six horizons were picked with their respective velocities to this level. Analysis of the data indicated that the two major units exhibiting interval velocity variation were the Pliocene "low velocity layer" and the Eocene carbonates. Using the smoothed stacking velocity down to the Top Palaeocene Calcilutite the three wells tied the depth conversion with an accuracy of 0.5%. Below this horizon two constant interval velocities were used from well data as the quality of the seismic pick were not as reliable. To verify this model BHPP also undertook a "layer-cake" velocity approach which, although confirming the anomalous zones, could not be used laterally away from the three wells, which unfortunately all lay in a straight line. Two wells, Puffin-4 and Parry-1 were drilled in 1988 to test the resultant interpretation. The wells intersected the Top Palaeocene Calcilutite within 1% of prognosis at Puffin-4 and within 2.2% of prognosis at Parry-1, therefore confirming the stacking velocity model used in depth conversion. However, both wells came in deep to prognosis at the deeper, objective level as a result, in the case of Puffin-4, of being on the downthrown side of a small fault, and at Parry-1 due to a thickening of the Paleocene section and seismic mispicking of the Top Palaeocene Calcilutite. Had the mispick at Parry-1 been avoided then the tie would have been less than 1.0%. Both these mis-interpretations were made in the part of the section where the quality of seismic was poorest. These two results suggest that even though the depth conversion to the Top Paleocene Calcilutite is accurate to within 1%, the magnitude of the velocity variation is larger than the magnitude of the independent depth closure. The Puffin Field requires both better quality seismic below the Base Palaeocene Calcilutite, or the means to resolve the lateral extent and possible thickness of a 4 m sand away from Puffin-2. Until such a method of obtaining either better quality seismic to the objective level, or to be able to define the seismic resolution of the differing sand bodies of a minimum size of 4 m, the Puffin Field will remain a Geophysical enigma.

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Kinematics of common-image gathers — Part 1: Theory

Geophysics ◽

10.1190/geo2018-0706.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. S437-S447 ◽

Cited By ~ 2

Author(s):

Jean-Philippe Montel ◽

Gilles Lambaré

Keyword(s):

Velocity Model ◽

General Assumption ◽

Azimuth Angle ◽

Velocity Analysis ◽

Migration Process ◽

Kinematic Behavior ◽

Migration Velocity Analysis ◽

Model Update ◽

The Common

Common-image gathers are a useful output of the migration process. Their kinematic behavior (i.e., the way they curve up or down) is an indicator of the quality of the velocity model used for migration. Traditionally, when used for migration velocity analysis, we pick structural dips in the common attribute panels (offset, angle, etc.) and residual moveout (RMO) in the gathers. The measured RMO will then tell us how much we need to update the velocity model to improve the gather’s flatness. Understanding the kinematics of the picked events is the key to an accurate model update. This point has been widely underestimated in many cases. For example, when dealing with angle gathers, there is a general assumption that the associated tomographic rays are fully defined by the picked structural dips and the gather opening and azimuth angle, and that if the velocity model is correctly updated down to a given horizon, it is not necessary to shoot the tomographic rays upward through this horizon. We find through an original theoretical analysis that both of these assumptions have to be modified when the gathers exhibit RMO. Using a kinematic analysis, we determine that knowledge of the RMO slopes is necessary to compute the tomographic rays.

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Velocity estimation by beam stack

Geophysics ◽

10.1190/1.1443315 ◽

1992 ◽

Vol 57 (8) ◽

pp. 1034-1047 ◽

Cited By ~ 27

Author(s):

Biondo Biondi

Keyword(s):

Estimation Method ◽

Velocity Model ◽

Velocity Estimation ◽

Gradient Algorithm ◽

Stratified Medium ◽

Detailed Knowledge ◽

Low Velocity ◽

Velocity Anomaly ◽

Interval Velocity ◽

Lateral Variations

Imaging seismic data requires detailed knowledge of the propagation velocity of compressional waves in the subsurface. In conventional seismic processing, the interval velocity model is usually derived from stacking velocities. Stacking velocities are determined by measuring the coherency of the reflections along hyperbolic moveout trajectories in offset. This conventional method becomes inaccurate in geologically complex areas because the conversion of stacking velocities to interval velocities assumes a horizontally stratified medium and mild lateral variations in velocity. The tomographic velocity estimation proposed in this paper can be applied when there are dipping reflectors and strong lateral variations. The method is based on the measurements of moveouts by beam stacks. A beam stack measures local coherency of reflections along hyperbolic trajectories. Because it is a local operator, the beam stack can provide information on nonhyperbolic moveouts in the data. This information is more reliable than traveltimes of reflections picked directly from the data because many seismic traces are used for computing beam stacks. To estimate interval velocity, I iteratively search for the velocity model that best predicts the events in beam‐stacked data. My estimation method does not require a preliminary picking of the data because it directly maximizes the beam‐stack’s energy at the traveltimes and surface locations predicted by ray tracing. The advantage of this formulation is that detection of the events in the beam‐stacked data can be guided by the imposition of smoothness constraints on the velocity model. The optimization problem of maximizing beam‐stack energy is solved by a gradient algorithm. To compute the derivatives of the objective function with respect to the velocity model, I derive a linear operator that relates perturbations in velocity to the observed changes in the beam‐stack kinematics. The method has been successfully applied to a marine survey for estimating a low‐velocity anomaly. The estimated velocity function correctly predicts the nonhyperbolic moveouts in the data caused by the velocity anomaly.

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Influence of structural dip angles on interval velocity analysis

Geophysics ◽

10.1190/1.2918741 ◽

2008 ◽

Vol 73 (4) ◽

pp. U13-U18 ◽

Cited By ~ 15

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.

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From migration to inversion velocity analysis

Geophysics ◽

10.1190/geo2016-0359.1 ◽

2017 ◽

Vol 82 (3) ◽

pp. S207-S223 ◽

Cited By ~ 18

Author(s):

Hervé Chauris ◽

Emmanuel Cocher

Keyword(s):

Objective Function ◽

Synthetic Data ◽

Velocity Model ◽

Specific Power ◽

Model Parameters ◽

Data Sets ◽

Velocity Analysis ◽

Low Velocity ◽

Image Domain ◽

New Approach

Migration velocity analysis (MVA) is a technique defined in the image domain to determine the background velocity model controlling the kinematics of wave propagation. In the presence of discontinuous interfaces, the velocity gradient used to iteratively update the velocity model exhibits spurious oscillations. For more stable results, we replace the migration part by an inversion scheme. By definition, migration is the adjoint of the Born modeling operator, whereas inversion is its asymptotic inverse. We have developed new expressions in 1D and 2D cases based on two-way wave-equation operators. The objective function measures the quality of the images obtained by inversion in the extended domain depending on the subsurface offset. In terms of implementation, the new approach is very similar to classic MVA. A 1D analysis found that oscillatory terms around the interface positions can be removed by multiplying the inversion result with the velocity at a specific power before evaluating the objective function. Several 2D synthetic data sets are discussed through the computation of the gradient needed to update the model parameters. Even for discontinuous reflectivity models, the new approach provides results without artificial oscillations. The model update corresponds to a gradient of an existing objective function, which was not the case for the horizontal contraction approach proposed as an alternative to deal with gradient artifacts. It also correctly handles low-velocity anomalies, contrary to the horizontal contraction approach. Inversion velocity analysis offers new perspectives for the applicability of image-domain velocity analysis.

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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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Interval velocity estimation via NMO-based differential semblance

Geophysics ◽

10.1190/1.2767401 ◽

2007 ◽

Vol 72 (6) ◽

pp. U75-U88 ◽

Cited By ~ 19

Author(s):

Jintan Li ◽

William W. Symes

Keyword(s):

Velocity Model ◽

Velocity Estimation ◽

Imaging Method ◽

Velocity Analysis ◽

Multiple Reflections ◽

Coherent Noise ◽

Interval Velocity ◽

Velocity Models ◽

Mean Square Difference ◽

Rich Data

The differential semblance method of velocity analysis flattens image gathers automatically by updating interval velocity to minimize the mean square difference of neighboring traces. We detail an implementation using hyperbolic normal moveout correction as the imaging method. The algorithm is fully automatic, accommodates arbitrary acquisition geometry, and outputs 1D, 2D, or 3D interval velocity models. This variant of differential semblance velocity analysis is effective within the limits of its imaging methodology: mild lateral heterogeneity and data dominated by primary events. Coherent noise events such as multiple reflections tend to degrade the quality of the velocity model estimated by differential semblance. We show how to combine differential semblance velocity analysis with dip filtering to suppress multiple reflections and thus improve considerably the accuracy of the velocity estimate. We illustrate this possibility using multiple-rich data from a 2D marine survey.

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Interval velocity analysis in the dip-angle domain

Geophysics ◽

10.1190/1.2957944 ◽

2008 ◽

Vol 73 (5) ◽

pp. VE353-VE360 ◽

Cited By ~ 8

Author(s):

Moshe Reshef

Keyword(s):

Real Data ◽

Velocity Model ◽

Velocity Analysis ◽

Standard Analysis ◽

Scattering Angle ◽

Angle Measure ◽

Interval Velocity ◽

Resultant Velocity ◽

Dip Angle ◽

Higher Sensitivity

When interval velocity analysis is conducted over complex geologic regions, scattering-angle gathers may cause significant inaccuracies. These inaccuracies are related to the loss of structural dip information when generating common-image gathers (CIGs). In this study, the idea of performing interval velocity analysis in the dip-angle domain was examined and demonstrated with synthetic and real data examples. The effects of migration velocity errors and their identification in this domain were analyzed in detail. Carrying the analysis directly on dip-angle gathers is practically impossible. The ability to perform a standard analysis based on flattening the events in the CIGs is achieved by replacing the dip-angle measure with an equivalent offset measure. This equivalent offset provides higher sensitivity to velocity errors and may improve the accuracy of the resultant velocity model.

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Tau migration and velocity analysis: Theory and synthetic examples

Geophysics ◽

10.1190/1.1598126 ◽

2003 ◽

Vol 68 (4) ◽

pp. 1331-1339 ◽

Cited By ~ 20

Author(s):

Tariq Alkhalifah

Keyword(s):

Time Domain ◽

Synthetic Data ◽

Velocity Model ◽

Migration Velocity ◽

Surface Velocity ◽

Velocity Analysis ◽

Near Surface ◽

Prestack Migration ◽

Interval Velocity ◽

Migration Velocity Analysis

Prestack migration velocity analysis in the time domain reduces the velocity‐depth ambiguity usually hampering the performance of prestack depth‐migration velocity analysis. In prestack τ migration velocity analysis, we keep the interval velocity model and the output images in vertical time. This allows us to avoid placing reflectors at erroneous depths during the velocity analysis process and, thus, avoid slowing down its convergence to the true velocity model. Using a 1D velocity update scheme, the prestack τ migration velocity analysis performed well on synthetic data from a model with a complex near‐surface velocity. Accurate velocity information and images were obtained using this time‐domain method. Problems occurred only in resolving a thin layer where the low resolution and fold of the synthetic data made it practically impossible to estimate velocity accurately in this layer. This 1D approach also provided us reasonable results for synthetic data from the Marmousi model. Despite the complexity of this model, the τ domain implementation of the prestack migration velocity analysis converged to a generally reasonable result, which includes properly imaging the elusive top‐of‐the‐reservoir layer.

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Description of a scanless method of stacking velocity analysis

Geophysics ◽

10.1190/1.1443375 ◽

1993 ◽

Vol 58 (11) ◽

pp. 1596-1606 ◽

Cited By ~ 2

Author(s):

Hans J. Tieman

Keyword(s):

Empirical Studies ◽

Real Data ◽

Velocity Variation ◽

Velocity Analysis ◽

Velocity Control ◽

Lateral Velocity ◽

Interval Velocity ◽

Offset Time ◽

Zero Offset ◽

Traditional Approaches

Stacking velocities can be directly estimated from seismic data without recourse to a multivelocity stack and subsequent search techniques that many current procedures use. This is done as follows: (1) apply NMO to the data (over a window, for a particular common midpoint) using initial estimates for zero offset time and velocity; (2) produce two stacks by summing the data over offset after applying different weighting functions; (3) cross correlate the two stacks; and (4) translate the lag into velocity and time updates. The procedure is iterated until convergence has occurred. Referred to as ARAMVEL (U.S. Patent No. 4,813,027), the method is best implemented as an interactive continuous velocity analysis. Although very simple, both empirical studies and theoretical analysis have shown that it determines velocities more accurately than more traditional approaches based on a scan approach. Convergence is fast, with only one or two iterations usually necessary. The method is robust, as only approximate information is necessary initially. Results with real data show that the method can economically give the detailed velocity control necessary for processing data from areas with considerable lateral velocity variation, as well as provide traveltime information that can be used for sophisticated inversion into interval velocity and depth.

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