Processing, inversion, and interpretation of a 2D seismic data set from the North Viking Graben, North Sea

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 837-848 ◽  
Author(s):  
Gislain B. Madiba ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Dynamic Range ◽  
Velocity Ratio ◽  
Total Porosity ◽  
Shear Velocity ◽  
Seismic Section ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Fluid Content ◽  
The North

Simultaneous elastic impedance inversion is performed on the 2D North Viking Graben seismic data set used at the 1994 SEG workshop on amplitude variation with offset and inversion. P‐velocity (Vp), S‐velocity (Vs), density logs, and seismic data are input to the inversion. The inverted P‐impedance and S‐impedance sections are used to generate an approximate compressional‐to‐shear velocity ratio (Vp/Vs) section which, in turn, is used along with water‐filled porosity (Swv) derived from the logs from two wells, to generate fluid estimate sections. This is possible as the reservoir sands have fairly constant total porosity of approximately 28 ± 4%, so the hydrocarbon filled porosity is the total porosity minus the water‐filled porosity. To enhance the separation of lithologies and of fluid content, we map Vp/Vs into Swv using an empirical crossplot‐derived relation. This mapping expands the dynamic range of the low end of the Vp/Vs values. The different lithologies and fluids are generally well separated in the Vp/Vs–Swv domain. Potential hydrocarbon reservoirs (as calibrated by the well data) are identified throughout the seismic section and are consistent with the fluid content estimations obtained from alternative computations. The Vp/Vs–Swv plane still does not produce unique interpretation in many situations. However, the critical distinction, which is between hydrocarbon‐bearing sands and all other geologic/reservoir configurations, is defined. Swv ≤ 0.17 and Vp/Vs ≤ 1.8 are the criteria that delineate potential reservoirs in this area, with decreasing Swv indicating a higher gas/oil ratio, and decreasing Vp/Vs indicating a higher sand/shale ratio. As these criteria are locally calibrated, they appear to be valid locally; they should not be applied to other data sets, which may exhibit significantly different relationships. However, the overall procedure should be generally applicable.

Quantifying the uncertainty in an AVO interpretation

Geophysics ◽  
2002 ◽  
Vol 67 (1) ◽  
pp. 117-125 ◽  
Author(s):  
Richard T. Houck
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reflection Coefficients ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Full Characterization ◽  
The North ◽  
The North Sea

Lithologic interpretations of amplitude variation with offset (AVO) information are ambiguous both because different lithologies occupy overlapping ranges of elastic properties, and because angle‐dependent reflection coefficients estimated from seismic data are uncertain. This paper presents a method for quantifying and combining these two components of uncertainty to get a full characterization of the uncertainty associated with an AVO‐based lithologic interpretation. The result of this approach is a compilation of all the lithologies that are consistent with the observed AVO behavior, along with a probability of occurrence for each lithology. A 2‐D line from the North Sea illustrates how the method might be applied in practice. For any data set, the interaction between the geologic and measurement components of uncertainty may significantly affect the overall uncertainty in a lithologic interpretation.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Seismic characterization of a blocky mass-transport deposit in the Trealla Limestone Formation, North Carnarvon Basin, Australia

2020 ◽  
Vol 8 (4) ◽  
pp. SR53-SR58
Author(s):  
Laura Ortiz-Sanguino ◽  
Javier Tellez ◽  
Heather Bedle ◽  
Dilan Martinez-Sanchez
Keyword(s):  
Mass Transport ◽  
Seismic Data ◽  
Information Management System ◽  
Data Set ◽  
Differential Dissolution ◽  
Passive Margins ◽  
The North ◽  
Seismic Characterization ◽  
The Individual ◽  
Carnarvon Basin

The deepwater Cenozoic strata in the North Carnarvon Basin, Australia, represent an interval of interest for stratigraphic studies in passive margins settings of mixed siliciclastic-carbonate environments. We have explored the geomorphological characteristics of a mass-transport deposit (MTD) within the Trealla Limestone Formation to describe in detail the differences among the blocks. To characterize the individual geometry and structural configuration of the blocks within the MTD, we used geometric seismic attributes such as coherence, curvature, dip azimuth, and dip magnitude using horizon slices and vertical profiles. The evaluation finds two types of blocks: remnant and glide (or rafted) blocks. Remnant blocks are in situ and stratigraphically continuous fragments with the underlying strata. This type of block is frequently fault-bounded and displays low deformation evidence. Glide blocks are part of the transported material detached from a paleoslope. These blocks are deformed and occasionally appear as “floating” fragments embedded within a chaotic matrix in the MTD. Glide blocks are used as kinematic indicators of the direction of deposition of MTDs. We evaluate these elements in a modern continental analog that resembles a similar setting for a better understanding of the slide occurrence. Geological feature: Glide blocks, North Carnarvon Basin, Australia Seismic appearance: Discrete angular blocks with internal reflectors Alternative interpretations: Differential dissolution in a mixed siliciclastic-carbonate environment Features with a similar appearance: Carbonate buildups, differential dissolution blocks Formation: Trealla Limestone Formation, North Carnarvon Basin Age: Early-Middle Miocene Location: Offshore Northwest Australia, North Carnarvon Basin Seismic data: Obtained from Western Australian Petroleum and Geothermal Information Management System, Draeck 3D seismic data set Analysis tools: Visualization software (Petrel 2019) and attribute performance software (AASPI 6.0)

Improving seismic QP estimation using rock-physics constraints

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. MR187-MR198 ◽  
Author(s):  
Yi Shen ◽  
Jack Dvorkin ◽  
Yunyue Li
Keyword(s):  
Seismic Data ◽  
Closed Form Solution ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Analytical Relation ◽  
Gas Reservoir ◽  
Seismic Section ◽  
Data Set ◽  
Attenuation Model ◽  
Text Model

Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.

Fracture detection via correlating P-wave amplitude variation with offset and azimuth analysis and well data in eastern central Saudi Arabia

2017 ◽  
Vol 5 (4) ◽  
pp. T531-T544
Author(s):  
Ali H. Al-Gawas ◽  
Abdullatif A. Al-Shuhail
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Production Data ◽  
Fracture Zones ◽  
Fracture Detection ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Well Data ◽  
Patch Geometry

The late Carboniferous clastic Unayzah-C in eastern central Saudi Arabia is a low-porosity, possibly fractured reservoir. Mapping the Unayzah-C is a challenge due to the low signal-to-noise ratio (S/N) and limited bandwidth in the conventional 3D seismic data. A related challenge is delineating and characterizing fracture zones within the Unayzah-C. Full-azimuth 3D broadband seismic data were acquired using point receivers, low-frequency sweeps down to 2 Hz, and 6 km patch geometry. The data indicate significant enhancement in continuity and resolution of the reflection data, leading to improved mapping of the Unayzah-C. Because the data set has a rectangular patch geometry with full inline offsets to 6000 m, using amplitude variation with offset and azimuth (AVOA) may be effective to delineate and characterize fracture zones within Unayzah-A and Unayzah-C. The study was undertaken to determine the improvement of wide-azimuth seismic data in fracture detection in clastic reservoirs. The results were validated with available well data including borehole images, well tests, and production data in the Unayzah-A. There are no production data or borehole images within the Unayzah-C. For validation, we had to refer to a comparison of alternative seismic fracture detection methods, mainly curvature and coherence. Anisotropy was found to be weak, which may be due to noise, clastic lithology, and heterogeneity of the reservoirs, in both reservoirs except for along the western steep flank of the study area. These may correspond to some north–south-trending faults suggested by circulation loss and borehole image data in a few wells. The orientation of the long axis of the anisotropy ellipses is northwest–southeast, and it is not in agreement with the north–south structural trend. No correlation was found among the curvature, coherence, and AVOA in Unayzah-A or Unayzah-C. Some possible explanations for the low correlation between the AVOA ellipticity and the natural fractures are a noisy data set, overburden anisotropy, heterogeneity, granulation seams, and deformation.

Hierarchical exploration of single station seismic data with unsupervised learning

2021 ◽  
Author(s):  
René Steinmann ◽  
Leonard Seydoux ◽  
Michel Campillo
Keyword(s):  
Unsupervised Learning ◽  
Seismic Data ◽  
Acoustic Emissions ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Seismic Waveforms ◽  
The North ◽  
Signal Features ◽  
Single Station ◽  
Seismic Crisis

<p>Seismic datasets contain an enormous amount of information and a large variety of signals with different origins. We usually observe signatures of earthquakes, volcanic and non-volcanic tremors, rockfalls, road and air traffic, atmospheric perturbations and many other acoustic emissions. More and more seismic sensors are deployed worldwide and record the seismic wavefield in a continuous fashion, generating massive volumes of data that cannot be analyzed manually in decent times. Therefore, identifying classes of signals in seismic data with automatic strategies is a crucial stage towards the understanding of the underlying physics of geological objects. For that reason seismologists have developed different tools to detect and classify certain types of signals. Recently, machine learning gained much attention due to its ability to recognize patterns. While supervised learning is a great tool for detecting and classifying signals within already-known classes, it cannot be used to infer new classes of signals, and can be strongly biased by the labels we impose. We here propose to overcome this limitation with unsupervised learning. In this study, we present a new way to explore single-station continuous seismic data with a dendrogram produced by agglomerative clustering. Our method is motivated by the idea that labels in a seismic data set follow a hierarchical order with different levels of details. For example earthquakes belong to the larger class of stationary signals and can be also divided into subclasses with different focal mechanism or magnitudes. We first use a scattering network (a convolutional neural network that makes use of wavelet filers) in order to extract a multi-scale representation of the continuous seismic waveforms. We then select the most meaningful features by means of independent component analysis, and apply an agglomerative clustering on this representation. We finally explore the dendrogram in a systematic way in order to explore the different signal classes revealed by the strategy. We illustrate our method on seismic data continuously recorded in the vicinity of the North-Anatolian fault, in Turkey. During this time period, a seismic crisis with more than 200 micro-earthquakes occurred, together with many other anthropogenic and meteorological events. By exploring the classes revealed by the dendrogram with <em>a posteriori</em> signal features (occurrence, within-class correlations, etc.) we show that the strategy is capable of retrieving the seismic crisis as well as signals related to anthropogenic and meteorogical activities.</p>

Nonstretch moveout correction of long-offset multichannel seismic data for subbasalt imaging: Example from the North Atlantic

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. R83-R91 ◽  
Author(s):  
Hassan Masoomzadeh ◽  
Penny J. Barton ◽  
Satish C. Singh
Keyword(s):  
North Atlantic ◽  
Seismic Data ◽  
Cost Effective ◽  
Data Set ◽  
Velocity Function ◽  
The North ◽  
Long Offset ◽  
The North Atlantic ◽  
New Processing ◽  
Multichannel Seismic Data

We have developed a pragmatic new processing strategy to enhance seismic information obtained from long-offset multichannel seismic data. The conventional processing approach, which treats data on a sample-by-sample basis, is applied at a coarser scale on groups of samples. Using this approach, a reflected event and its vicinity remain unstretched during the normal moveout correction. Isomoveout curves (lines of equal moveout) in the time-velocity panel are employed to apply a constant moveout correction to selected individual events, leading to a nonstretch stack. A zigzag stacking-velocity function is introduced as a combination of segments of appropriate isomoveout curves. By employing a zigzag velocity function, stretching of key events is avoided and thus information at far offset is preserved in the stack. The method is also computationally cost-effective. However, the zigzag stacking-velocity field must be consistent with target horizons. This method of horizon-consistent nonstretch moveout has been applied to a wide-angle data set from the North Atlantic margin, providing improved images of the basement interface, which was previously poorly imaged.

Application of very fast simulated annealing and differential evolution in the search for FO-CRS wavefield attributes

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. O81-O92
Author(s):  
German Garabito ◽  
João Carlos R. Cruz
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Seismic Data ◽  
Optimization Algorithms ◽  
Two Dimensions ◽  
Seismic Section ◽  
Data Set ◽  
Computational Costs ◽  
Very Fast Simulated Annealing

The finite-offset common-reflection-surface (FO-CRS) stack method can be used to simulate any common-offset (CO) seismic section by stacking prestack seismic data along the surfaces defined by the paraxial hyperbolic traveltime approximation. In two dimensions, the FO-CRS stacking operator depends on five kinematic wavefield attributes for every time sample of the target CO section. The main problem with this method is identifying a computationally efficient data-driven search strategy for accurately determining the best set of FO-CRS attributes that produce the optimal coherence measure of the seismic signal in the prestack data. Identifying a global optimization algorithm with the best performance is a challenge when solving this optimization problem. This is because the objective function is multimodal and involves a large volume of data, which leads to high computational costs. We introduced a comparative and competitive study through the application of two global optimization algorithms that simultaneously search the FO-CRS attributes from the prestack seismic data, very fast simulated annealing (VFSA) and the differential evolution (DE). By applying this FO-CRS stack to the Marmousi synthetic seismic data set, we have compared the performances of the two optimization algorithms with regard to their efficiency and effectiveness in estimating the five FO-CRS attributes. To analyze the robustness of the two algorithms, we apply them to real land seismic data and show their ability to find the near-optimal attributes and to improve reflection events in noisy data with a very low fold. We reveal that VFSA is efficient in reaching the optimal coherence value with the lowest computational costs, and that DE is effective and reliable in reaching the optimal coherence for determining the best five searched-for attributes. Regardless of the differences, the FO-CRS stack produces enhanced and regularized high-quality CO sections using both global optimization methods.

Amplitude-variation-with-offset and prestack-waveform inversion: A direct comparison using a real data example from the Rock Springs Uplift, Wyoming, USA

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. B45-B59 ◽  
Author(s):  
Subhashis Mallick ◽  
Samar Adhikari
Keyword(s):  
Wave Equation ◽  
Seismic Data ◽  
Quantitative Estimation ◽  
Waveform Inversion ◽  
Forward Modeling ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Rock Springs Uplift ◽  
Rock Springs

Recent advances in seismic data acquisition and processing allow routine extraction of offset-/angle-dependent reflection amplitudes from prestack seismic data for quantifying subsurface lithologic and fluid properties. Amplitude-variation-with-offset (AVO) inversion is the most commonly used practice for such quantification. Although quite successful, AVO has a few shortcomings primarily due to the simplicity in its inherent assumptions, and for any quantitative estimation of reservoir properties, they are generally interpreted in combination with other information. In recent years, waveform-based inversions have gained popularity in reservoir characterization and depth imaging. Going beyond the simple assumptions of AVO and using wave equation solutions, these methods have been effective in accurately predicting the subsurface properties. Developments of these waveform inversions have so far been along two lines: (1) the methods that use a locally 1D model of the subsurface for each common midpoint and use an analytical solution to the wave equation for forward modeling and (2) the methods that do not make any 1D assumption but use an approximate numerical solution to the wave equation in 2D or 3D for forward modeling. Routine applications of these inversions are, however, still computationally demanding. We described a multilevel parallelization of elastic-waveform inversion methodology under a 1D assumption that allowed its application in a reasonable time frame. Applying AVO and waveform inversion on a single data set from the Rock Springs Uplift, Wyoming, USA, and comparing them with one another, we also determined that the waveform-based method was capable of obtaining a much superior description of subsurface properties compared with AVO. We concluded that the waveform inversions should be the method of choice for reservoir property estimation as high-performance computers become commonly available.

The Brent Field, Block 211/29, UK North Sea

1991 ◽  
Vol 14 (1) ◽  
pp. 63-72 ◽  
Author(s):  
A. P. Struijk ◽  
R. T. Green
Keyword(s):  
North Sea ◽  
Water Depth ◽  
Seismic Data ◽  
Middle Jurassic ◽  
Joint Venture ◽  
Gas Injection ◽  
Lower Jurassic ◽  
Data Set ◽  
North East ◽  
The North

AbstractThe Brent Field was the first discovery in the northern part of the North Sea, and is one of the largest hydrocarbon accumulations in the United Kingdom licence area. There are two separate major accumulations: one in the Middle Jurassic (Brent Group reservoir) and one in the Lower Jurassic/Triassic (Statfjord Formation reservoir). The field lies entirely within UK licence Block 211/29 at latitude 61°N and longitude 2°E. The water depth is 460 ft. The discovery well was drilled in 1971, and six further exploration and appraisal wells were drilled. Seismic data over the Brent Field has been acquired in three separate vintages. The latest acquisition is a 3-D grid recorded in 1986. Reprocessing of the entire 1986 3-D seismic data set was initiated in 1989.The original oil/condensate-in-place, estimated on 1/1/89, is 3500 MMBBL, and the estimated original wet gas-in-place is 6700 TCF. Oil production is now in the decline phase. Average production in 1988 was 334,000 BOPD, with gas sales remaining at the plateau rate of 500 MMSCFD.The field is being developed from four fixed platforms, each providing production, water injection and gas injection facilities for both Brent and Statfjord Formation reservoirs. Gas injection is distributed to achieve an intermediate oil rim development in some reservoir units. The platforms were installed between 1975 and 1978. Production commenced in 1976. The slump faulted crestal areas of both reservoirs have yet to be developed.These crestal areas contain about 5% of the recoverable reserves. Appraisal drilling was carried out in the crest during 1988 and 1989.The Brent Field is located approximately 100 miles north-east of the Shetland Islands and 300 miles NNE of Aberdeen (Fig. 1). The discovery well location is at latitude 61°05'53.87" North longitude 1°41'30.H" East. The water depth is 460 ftThe field comprises two distinct reservoirs, the Brent Group and the Statfjord Formation, which are of Middle Jurassic and Lower Jurassic/Triassic age respectively. The reservoirs occur in a westerly dipping tilted fault block in a fault controlled unconformity trap (Fig. 2).The size of the hydrocarbon bearing area is approximately 10 miles from north to south and 3 miles from east to west (Fig. 3).The reservoirs are in turn divided into seven separate reservoir units; four cycles in the Brent Group reservoir and three units in the Statfjord Formation reservoir. Laterally two major east-west orientated faults divide the field into three separate production areas. A fourth area is the north-south orientated crestal part of both reservoirs, which is faulted and has a series of down faulted slump blocks overlain by 'Reworked Sediment'. This area still has to be developed.The Shell/Esso joint venture North Sea oilfields are named after water and waterside birds. The Brent Field is named after the Brent goose.

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