Using a fine-tuned interval velocity model for improving the quality of time-to-depth conversion

2009 ◽  
Vol 28 (12) ◽  
pp. 1430-1434 ◽  
Author(s):  
Reinaldo Viloria ◽  
Ismael Garcia ◽  
Adrien Caudron ◽  
Rafael Cariel ◽  
Flavio De Caires ◽  
...  
Keyword(s):  
Velocity Model ◽  
Interval Velocity

scholarly journals The Puffin Field ? a geophysical enigma

1989 ◽  
Vol 20 (2) ◽  
pp. 301
Author(s):  
P.D. Grant
Keyword(s):  
Joint Venture ◽  
Source Area ◽  
Velocity Model ◽  
Minimum Size ◽  
Velocity Variation ◽  
Velocity Analysis ◽  
Low Velocity ◽  
Interval Velocity ◽  
Oil Water

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.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

Kinematics of common-image gathers — Part 1: Theory

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S437-S447 ◽  
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.

Automatic, geologic layer-constrained well-seismic tie through blocked dynamic warping

2017 ◽  
Vol 5 (3) ◽  
pp. SJ81-SJ90 ◽  
Author(s):  
Kainan Wang ◽  
Jesse Lomask ◽  
Felix Segovia
Keyword(s):  
Seismic Data ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Velocity Model ◽  
Data Set ◽  
Oil And Gas Exploration ◽  
Interval Velocity ◽  
Velocity Changes ◽  
Dynamic Warping

Well-log-to-seismic tying is a key step in many interpretation workflows for oil and gas exploration. Synthetic seismic traces from the wells are often manually tied to seismic data; this process can be very time consuming and, in some cases, inaccurate. Automatic methods, such as dynamic time warping (DTW), can match synthetic traces to seismic data. Although these methods are extremely fast, they tend to create interval velocities that are not geologically realistic. We have described the modification of DTW to create a blocked dynamic warping (BDW) method. BDW generates an automatic, optimal well tie that honors geologically consistent velocity constraints. Consequently, it results in updated velocities that are more realistic than other methods. BDW constrains the updated velocity to be constant or linearly variable inside each geologic layer. With an optimal correlation between synthetic seismograms and surface seismic data, this algorithm returns an automatically updated time-depth curve and an updated interval velocity model that still retains the original geologic velocity boundaries. In other words, the algorithm finds the optimal solution for tying the synthetic to the seismic data while restricting the interval velocity changes to coincide with the initial input blocking. We have determined the application of the BDW technique on a synthetic data example and field data set.

Velocity estimation by beam stack

Geophysics ◽  
1992 ◽  
Vol 57 (8) ◽  
pp. 1034-1047 ◽  
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.

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.

Improved seismic interpretation of a salt diapir by utilization of diffractions, exemplified by 2D reflection seismics, Danish sector of the North Sea

2020 ◽  
Vol 8 (1) ◽  
pp. T77-T88 ◽  
Author(s):  
Mahboubeh Montazeri ◽  
Lars Ole Boldreel ◽  
Anette Uldall ◽  
Lars Nielsen
Keyword(s):  
North Sea ◽  
Seismic Imaging ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Salt Diapir ◽  
The North ◽  
Reflection Seismics ◽  
The North Sea ◽  
Sediment Layers

Development of salt diapirs affects the hydrocarbon trapping systems in the Danish sector of the North Sea, where the reservoirs mainly consist of chalk. Seismic imaging and interpretation of the salt structures are challenging, primarily due to the complex geometry of the salt bodies and typically strong velocity contrast with the neighboring sediment layers. The quality of seismic imaging in the North Sea is highly dependent on the quality of the estimated velocity model. We have studied diffracted arrivals originating from the salt flanks and adjacent sedimentary structures using a diffraction imaging technique. The diffracted waves carry valuable information regarding seismic velocity and the location of geologic discontinuities, such as faults, fractures, and salt delimitations. We apply a plane-wave destruction method to separate diffractions from our stacked data. We optimize imaging based on diffraction analysis by using a velocity continuation migration technique, which leads to an estimation of the optimum focusing velocity model. We determine that the diffraction-based approach significantly improves the seismic imaging adjacent to the salt diapirs and the neighboring layers when compared with a standard approach in which we mostly ignore the diffractions. The new poststack time-migrated results provide detailed information that optimizes our interpretation of the salt diapir itself (e.g., the width of the salt neck) as well as the sediment layers related to the rim synclines. Processing schemes such as prestack depth migration and full-waveform inversion may potentially provide high-resolution images of the salt structures. We only account for diffractions in nonmigrated stacked data to better constrain seismic velocity and improve imaging around the salt diapir. The obtained results are critical for reservoir characterization.

Applications of the trend-match attribute for interpreting seismic velocity data

2019 ◽  
Vol 7 (2) ◽  
pp. SB1-SB10
Author(s):  
Mingya Chen ◽  
Damian O’Grady
Keyword(s):  
Case Studies ◽  
Seismic Velocity ◽  
Regional Scale ◽  
Velocity Model ◽  
Velocity Data ◽  
Thickness Estimation ◽  
Velocity Modeling ◽  
Modeling Techniques ◽  
Seismic Images

A seismic velocity model is one of the products generated from a seismic imaging project. Recent advances in velocity modeling techniques have significantly improved the quality of seismic velocity data. Yet, the use of seismic velocity to guide geologic interpretations is still limited. This is mainly due to the overwhelming effect of compaction and the low-resolution nature of the seismic velocity model. Geologic boundaries and anomalies are often difficult to visualize from seismic velocity data alone. A new attribute called the trend-match attribute is proposed to reveal changes in velocity compaction trends from seismic velocity. The attribute is computed by comparing seismic velocity data with the regional velocity depth trends defined for different lithofacies using wells. We applied the trend-match attribute on several case studies to facilitate stratigraphic interpretation, horizon mapping, and erosion thickness estimation. Integration of the trend-match attribute volume with migrated seismic images can further constrain the geologic and stratigraphic interpretation at a regional scale.

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