Seismic signature of a Swan Hills (Frasnian) reef reservoir, Snipe Lake, Alberta

Geophysics ◽  
1989 ◽  
Vol 54 (2) ◽  
pp. 148-157 ◽  
Author(s):  
N. L. Anderson ◽  
R. J. Brown ◽  
R. C. Hinds ◽  
L. V. Hills
Keyword(s):  
High Amplitude ◽  
Half Cycle ◽  
Areal Extent ◽  
Image Characteristics ◽  
Carbonate Buildups ◽  
Seismic Image ◽  
Frasnian Stage ◽  
Seismic Sections ◽  
Seismic Images ◽  
Lateral Variations

Swan Hills formation (Frasnian stage) carbonate buildups of the Beaverhill Lake group are generally of low relief and considerable areal extent and are overlain by and encased within the relatively high‐velocity shale of the Waterways formation, which thins but does not drape across the reefs. Consistent with this picture, prereef seismic events are not significantly pulled up beneath the reefs nor are postreef events draped across them. Indeed, the seismic images of these reefs are effectively masked by the high‐amplitude reflections from the overlying top of the Beaverhill Lake group and underlying Gilwood member and cannot be distinguished from those of the basin fill. However, it is possible to identify the reefs indirectly on conventionally processed seismic sections because the image of the encompassing Beaverhill Lake/Gilwood interval varies significantly from onreef to offreef positions. One such Swan Hills formation field at Snipe Lake has an areal extent of about [Formula: see text] and typical reef relief of some 50 m above the platform facies. This reef is shown to be recognizable on three example seismic lines from interference phenomena that vary laterally in association with the lateral variations in thickness of the Swan Hills formation. These phenomena include an offreef peak that is one half‐cycle below the Beaverhill Lake reflection trough and that dies out laterally going onreef, a tendency for the amplitude of the Gilwood event to decrease beneath the reef, and thinning of the order of 5 ms of the onreef section relative to the offreef section. Through seismic modeling, these seismic‐image characteristics are seen to be predictable geophysical manifestations of the inherent geologic variations.

A seismic analysis of Black Creek and Wabumun salt collapse features, western Canadian sedimentary basin

Geophysics ◽  
1991 ◽  
Vol 56 (5) ◽  
pp. 618-627 ◽  
Author(s):  
N. L. Anderson ◽  
R. J. Brown
Keyword(s):  
Seismic Data ◽  
Seismic Analysis ◽  
High Amplitude ◽  
Well Log ◽  
Areal Extent ◽  
Distribution Maps ◽  
Salt Distribution ◽  
Lateral Variations ◽  
Reservoir Facies

Two Devonian salts of western Canada, those of the Black Creek member (Upper Elk Point subgroup) in northwest Alberta and those of the Wabamun group in southeastern Alberta, were widely distributed and uniformly deposited within their respective basins. Both of these salts are interbedded within predominantly carbonate sequences and both have been extensively leached. They are now preserved as discontinuous remnants of variable thickness and areal extent. These salt remnants and their associated collapse features are often associated with structural or stratigraphic traps. Structural traps typically form where reservoir facies are closed across remnant salts, stratigraphic traps often develop where reservoir facies were either preferentially deposited and/or preserved in salt collapse lows. As a result of these relationships between dissolution and hydrocarbon entrapment, the distribution (areal extent and thickness) of these salt remnants is of significant interest to the explorationist. Both the Black Creek and Wabamun salts have relatively abrupt contacts with the encasing higher velocity, higher density carbonates. Where these salts are sufficiently thick, their top and base typically generate high amplitude reflections, and lateral variations in the salt isopach can be directly determined from the seismic data. Relative salt thicknesses can also be indirectly estimated through analyses of lateral variations in the thicknesses of the encompassing carbonates, time structural drape and velocity pullup. Such seismic information about the thickness and the extent of these salts should be used together with well log control to generate subsurface distribution maps. These maps will facilitate both the delineation of prospective structural and stratigraphic play fairways and the determination of the timing of salt dissolution. In addition, an appreciation of regional salt distribution will decrease the likelihood that remnant salts will be misinterpreted as either reefs and/or faulted structures.

scholarly journals Appraising structural interpretations using seismic data — Theoretical elements

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. N29-N40
Author(s):  
Modeste Irakarama ◽  
Paul Cupillard ◽  
Guillaume Caumon ◽  
Paul Sava ◽  
Jonathan Edwards
Keyword(s):  
Seismic Data ◽  
Structural Model ◽  
Synthetic Data ◽  
Target Region ◽  
Structural Interpretation ◽  
Vertical Seismic Profiling ◽  
Seismic Image ◽  
Vsp Data ◽  
Phase Shift Data ◽  
Seismic Images

Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

Complex seismic trace analysis of thin beds

Geophysics ◽  
1984 ◽  
Vol 49 (4) ◽  
pp. 344-352 ◽  
Author(s):  
James D. Robertson ◽  
Henry H. Nogami
Keyword(s):  
Instantaneous Frequency ◽  
Trace Analysis ◽  
Frequency Tuning ◽  
Seismic Energy ◽  
High Amplitude ◽  
Opposite Polarity ◽  
Peak Period ◽  
Seismic Wavelet ◽  
Porous Sandstone ◽  
Seismic Sections

Displays of complex trace attributes can help to define thin beds in seismic sections. If the wavelet in a section is zero phase, low impedance strata whose thicknesses are of the order of half the peak‐to‐peak period of the dominant seismic energy show up as anomalously high‐amplitude zones on instantaneous amplitude sections. These anomalies result from the well‐known amplitude tuning effect which occurs when reflection coefficients of opposite polarity a half period apart are convolved with a seismic wavelet. As the layers thin to a quarter period of the dominant seismic energy, thinning is revealed by an anomalous increase in instantaneous frequency. This behavior results from the less well‐known but equally important phenomenon of frequency tuning by beds which thin laterally. Instantaneous frequency reaches an anomalously high value when bed thickness is about a quarter period and remains high as the bed continues to thin. In this paper, complex trace analysis is applied to a synthetic model of a wedge and to a set of broadband field data acquired to delineate thin lenses of porous sandstone. The two case studies illustrate that sets of attribute displays can be used to verify the presence and dimensions of thin beds when definition of the beds is not obvious on conventional seismic sections.

GOLDEN BEACH: A BRIGHT SPOT

1978 ◽  
Vol 18 (1) ◽  
pp. 109
Author(s):  
R. B. Mariow
Keyword(s):  
Seismic Data ◽  
High Amplitude ◽  
Late Eocene ◽  
Polarity Reversal ◽  
Bright Spot ◽  
Areal Extent ◽  
Water Contact ◽  
Bright Spots ◽  
Gippsland Basin ◽  
Seismic Reflector

The Golden Beach closed anticlinal structure lies five kilometres offshore in the Gippsland Basin. Golden Beach 1A was drilled in 1967 near the crest of the structure and intersected a gas column of 19 m (63 feet) at the top of the Latrobe Group (Late Eocene) where most of the hydrocarbon accumulations in the Gippsland Basin have been found. The gas-water contact lies at a depth of 652 m (2139 feet) below sea level.On seismic data recorded over the structure, a high amplitude flat-lying event was interpreted as a bright 'flat spot' at the gas-water contact. Reprocessing of the seismic data enhanced the bright spot effect and enabled the areal extent of the gas zone to be mapped. The presence of the gas also leads to a polarity reversal of the top of the Latrobe Group seismic reflector over the gas accumulation.Seismic data from other structures containing hydrocarbons in the Gippsland Basin support the concept that bright spots and flat spots are more likely to be associated with gas than with oil accumulations, and that the observed bright spot effect decreases with increasing depth.

Detecting faults and channels while enhancing seismic structural and stratigraphic features

2019 ◽  
Vol 7 (1) ◽  
pp. T155-T166 ◽  
Author(s):  
Xinming Wu ◽  
Zhenwei Guo
Keyword(s):  
Iterative Scheme ◽  
3D Seismic ◽  
Linear Features ◽  
Smoothing Methods ◽  
Seismic Image ◽  
Diffusion Scheme ◽  
Seismic Reflections ◽  
2D And 3D ◽  
Seismic Images

A 3D seismic image contains structural and stratigraphic features such as reflections, faults, and channels. When smoothing such an image, we want to enhance all of these features so that they are easier to interpret. Most smoothing methods aim to enhance reflections but may blur faults and channels in the image. A few methods smooth seismic reflections while preserving faults and channel boundaries. However, it has not well-discussed to smooth simultaneously along the seismic reflections and channels, which are linear features apparent within dipping reflections. In addition, to interpret faults and channels, extra steps are required to compute attributes or mappings of faults and channels from a seismic image. Such fault and channel attributes are often sensitive to noise because they are typically computed as discontinuities of seismic reflections. In this paper, we have developed methods to simultaneously enhance seismic reflections, faults, and channels while obtaining mappings of the faults and channels. In these methods, we first estimate the orientations of the reflections, faults, and channels directly in a seismic image. We then use the estimated orientations to control the smoothing directions in an efficient iterative diffusion scheme to smooth a seismic image along the reflections and channels. In this iterative scheme, we also efficiently compute mappings of faults and channels, which are used to control smoothing extents in the diffusion to stop smoothing across them. This diffusion scheme iteratively smooths a seismic image along reflections and channels while updating the mappings of faults and channels. By doing this, we will finally obtain an enhanced seismic image (with enhanced reflections and channels and sharpened faults) and cleaned mappings of faults and channels (discontinuities related to noise are cleaned up). We have examined the methods using 2D and 3D real seismic images.

Improving fault surface construction with inversion-based methods

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. IM1-IM14
Author(s):  
Zhengfa Bi ◽  
Xinming Wu
Keyword(s):  
Least Squares ◽  
Poisson Equation ◽  
Scalar Function ◽  
Fault Surface ◽  
The Poisson Equation ◽  
Surface Method ◽  
Seismic Image ◽  
Point Set ◽  
Seismic Images ◽  
Surface Construction

Constructing fault surfaces is a key step for seismic structural interpretation and building structural models. We automatically construct fault surfaces from oriented fault samples scanned from a 3D seismic image. The main challenges of the fault surface construction include the following: Some fault samples are locally missing, the positions and orientations of the fault samples may be noisy, and surfaces may form complicated intersections with each other. We adopt the Poisson equation surface method (PESM) and the point-set surface method (PSSM) to automatically construct complete fault surfaces from fault samples and their corresponding orientations. Our methods can robustly fit the noisy fault samples and reasonably fill holes or missing samples, thus improving fault surface construction. By formulating fault surface construction as an inverse problem, we estimate a scalar function to approximate the fault samples in the least-squares sense. In PESM, we estimate the scalar function by solving a weighted Poisson equation. In PSSM, the scalar function is derived by fitting local algebraic spheres based on moving least-squares approximations. Then, the fault surfaces can be approximated by zero isosurfaces of the resulting scalar function. To handle complicated cases of crossing faults, we first classify the fault samples according to their orientations, and we take each class of samples as input of our inversion-based approaches to independently construct the crossing faults. We determine the ability of our methods in robustly building the complete fault surface using synthetic and real seismic images complicated by noise and complexly intersecting faults.

Edge-aware filtering with Siamese neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 711-717
Author(s):  
Mehdi Aharchaou ◽  
Michael Matheney ◽  
Joe Molyneux ◽  
Erik Neumann
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Learning Ability ◽  
Seismic Exploration ◽  
Data Sets ◽  
3D Seismic ◽  
New Class ◽  
Seismic Image ◽  
Siamese Networks ◽  
Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

Sensitivity of the dispersion correction to Q error

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 288-290 ◽  
Author(s):  
Richard E. Duren ◽  
E. Clark Trantham
Keyword(s):  
Amplitude Spectrum ◽  
Careful Attention ◽  
Dispersion Correction ◽  
Event Time ◽  
Seismic Wavelet ◽  
Reflection Time ◽  
Seismic Image ◽  
Time Zones ◽  
Seismic Sections ◽  
Zero Phase

A controlled‐phase acquisition and processing methodology for our company has been described by Trantham (1994). He pointed out that it is careful attention to wavelet phase that leads to improved well ties and a more geologically accurate seismic image. In addition, we prefer zero‐phase wavelets on our seismic sections. For a given amplitude spectrum they have the simplest shape and the highest peak; further, the peak occurs at the reflection time of the event. This alignment is important since the seismic wavelet generally broadens with increasing depth with a zero‐phase wavelet remaining symmetrical about the event time. Our experience has been that a true zero‐phase section can be tied over the entire length of a synthetic trace without having to slide the synthetic trace to tie different time zones.

Automatically interpreting all faults, unconformities, and horizons from 3D seismic images

2016 ◽  
Vol 4 (2) ◽  
pp. T227-T237 ◽  
Author(s):  
Xinming Wu ◽  
Dave Hale
Keyword(s):  
Image Processing ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Array Processing ◽  
3D Seismic ◽  
Partial Differential ◽  
Seismic Image ◽  
Processing Procedure ◽  
Seismic Images

Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although others automate the extraction of each type of these surfaces to some extent, it is difficult to automatically interpret a seismic image with all three types of surfaces because they could intersect with each other. For example, horizons can be especially difficult to extract from a seismic image complicated by faults and unconformities because a horizon surface can be dislocated at faults and terminated at unconformities. We have proposed a processing procedure to automatically extract all the faults, unconformities, and horizon surfaces from a 3D seismic image. In our processing, we first extracted fault surfaces, estimated fault slips, and undid the faulting in the seismic image. Then, we extracted unconformities from the unfaulted image with continuous reflectors across faults. Finally, we used the unconformities as constraints for image flattening and horizon extraction. Most of the processing was image processing or array processing and was achieved by efficiently solving partial differential equations. We used a 3D real example with faults and unconformities to demonstrate the entire image processing.

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