- Home
- A-Z Publications
- First Break
- Previous Issues
- Volume 20, Issue 3, 2002
First Break - Volume 20, Issue 3, 2002
Volume 20, Issue 3, 2002
-
-
High end visualization provides new dimensions in survey design and acquisition quality control
Authors B. Kajl, A. Long, J. Hoffma and S. StrandenesBarbara Kajl, Andrew Long, Jurgen Hoffma and, Sverre Strandenes (PGS Geophysical) discuss some illumination techniques aided by high end visualization technology used by PGS in the planning and quality control of 3D seismic surveys.
-
-
-
Volume interpretation of multi-attribute 3D surveys
More LessHuw James, Andy Peloso and Joanne Wang (Paradigm Geophysical) describe some of the technologies and methods that simplify and automate the interpretation of multiple attributes generated for a range of broad objectives when exploring for hydrocarbons with 3D seismic surveys.
-
-
-
Collaborative interpretation environments re-energized the workplace: so what’s the next phase?
Authors K. Tushingham and P. HodgsonKeith Tushingham and Phil Hodgson of Schlumberger Information Solutions, Houston look at what has been achieved in visualization technology to date and the shape of the future being mapped out by vendors and energy companies together.
-
-
-
Will internet seismic processing be the new paradigm for depth migration interpretation and visualization?
Authors D. Bevc, A.M. Popovici and B. BiondiDimitri Bevc and Alexander M. Popovici (3DGeo Development) and Biondo Biondi (Stanford University Exploration Project and the 3DGeo Development) discuss development work based on Internet technology which may make costly data visualization and interpretation processes more accessible to the exploration geoscience community.
-
-
-
Sea Bed Logging (SBL), a new method for remote and direct identification of hydrocarbon filled layers in deepwater areas
Authors T. Eidesmo, S. Ellingsrud, L.M. MacGregor, S. Constable, M.C. Sinha, S. E. Johansen, F.N. Kong and H. WesterdahlIn this paper we describe a technique called Sea Bed Logging (SBL), an application of marine CSEM sounding, which can be applied to detect and characterize hydrocarbon bearing reservoirs in deep water areas.
-
-
-
Where do P-S conversions occur? Analysis of OBS-data from the NE Atlantic Margin
Authors R. Mjelde, J.P. Fjellanger, T. Raum, S. Kodaira, A. Breivik, H. Shimamura and P. DigranesThe vast majority of seismic surveys on the Norwegian continental shelf have been performed using a towed streamer. The reason for this method’s dominance is its efficiency in 2D and 3D mapping of sedimentary structures of importance in the exploration for hydrocarbons. It is, however, curious that the first seismic surveys performed on the Norwegian continental shelf were conducted in 1962 using multicomponent geophones placed on the sea-floor (Hirschleber et al. 1966). Today, this is state-of-the-art in seismic exploration. During the last decade there has been a steady increase in interest in multicomponent ocean-bottom seismograph (OBS) surveys, and during the last 5 years there has been an even greater increase in interest in marine multicomponent reflection surveys (marine 4C). For both types of survey we use conventional marine seismic sources, but the multicomponent sensors, the way the sensors are handled and the acquisition geometry, are very different in the two methods. OBS surveys are characterized by a coarse receiver grid and coarse shooting over a large offset range. A typical receiver distance is 5–30 km and a typical offset range is from zero to several hundred kilometres. The sensors are dropped from the sea-surface to the sea-floor. The interest in OBS surveys has two main causes: • Wide-angle reflections and refractions (P-waves) can be used to map structures beneath volcanic rocks (e.g. Mjelde et al. 1992, 1997). • Various kinds of converted waves recorded as P- or S-waves at the sea-floor can be used to constrain lithology and pore fluid (e.g. Neidell 1985; Berg et al. 1997; Digranes et al. 1998). Marine 4C surveys are characterized by a relatively dense receiver grid and dense shooting over a moderate offset range (Caldwell 1999). A typical receiver distance is from 25 to a couple of hundred metres and a typical offset range is from zero to five kilometres. Most types of 4C sensor are incorporated in cables which are placed on the sea-floor by specialized surface vessels. Other sensor types are planted on the sea-floor by a remotely operated vehicle (ROV). The data are mainly used for imaging using reflected P-waves (PP) and converted waves (PS), and a critical assumption is that the conversions take place at the reflecting boundary. There are many applications for marine 4C data (MacLeod et al. 1999; Rognø et al. 1999); the most important are: • Improved imaging in PP due to lower background noise level, better azimuth distribution and more possibilities for removal of receiver ghost and multiples. • Imaging in PS below shallow gas and for geological boundaries with low contrast in acoustic impedance for P-waves. For shallow structures one can also achieve better vertical resolution in PS than for PP. • Lithology and fluid characterization from combined analysis of PP and PS data. In the imaging of marine 4C data it is commonly seen that both vertical and horizontal resolution degrade more rapidly with depth for PS than for PP. S-wave absorption is assumed to have some influence on this, but it is reasonable to assume that absorption is just one of many contributing causes. The PS wavefield has a tendency to become more complicated with depth. There are many probable reasons for this, but the most obvious is that the wavefield passes one or several interfaces which produce strong, transmitted converted waves. These transmitted converted waves produce seismic events with nearly the same apparent velocity as the PS events reflected at the corresponding depth. In some cases, it is difficult to identify which events are converted at the reflecting interface, and which are converted through transmission at nearby interfaces. Incorrect interpretation might lead to inconsistency in the processing velocities and smearing of the resulting image. Event identification can be guided by forward modelling, but it would be helpful if we knew beforehand what interfaces are likely to be the strongest P-to-S conversion interfaces. In wide angle OBS data, similarities between P- and S-wave refractive events can be used for direct detection of layers with S-wave propagation. The present paper describes the identification of conversion boundaries in modelling of both unpublished (presented in University reports) and published OBS data. The database covers volcanic sedimentary basins, nonvolcanic sedimentary basins, uplifted continental shelf, normal oceanic crust and thickened oceanic crust. A total of 2485 identified S-wave arrivals from a total of 347 OBSs along 8023 km of 2D profiles have been interpreted and modelled. The data sets used were acquired on the Faeroe, Møre, Vøring, Lofoten and western Barents Sea Margins, as well as in the north-eastern Barents Sea (Fig. 1). Our opinion is that the identification of conversion boundaries is more easily achieved with wide-angle OBS data than with the more conventional offset ranges used for processing of marine 4C data. However, the acquired marine 4C data usually contain long offsets, at least to one side of each receiver. We believe that the methodology presented in this paper can, in many cases, be used on marine 4C data, but this remains to be proven.
-
-
-
Coherent noise attenuation using inverse problems and prediction-error filters
By A. GuittonIn seismic, we can express many of the processing steps as linear operators. These operators perform a mapping of one domain, usually a model of the earth parameterized in terms of velocity, reflectivity, into another domain, usually seismic data sorted into CMP or shot gathers. This mapping is called modelling because it models the seismic data. Usually we desire the opposite of modelling, i.e. given some data, we want to retrieve the model. In many cases the adjoint of the modelling operator is used to estimate the model. For some operators, like the Fourier transform, the adjoint is the exact inverse; for others, the vast majority, the adjoint is not the true inverse but rather an approximation of the inverse. Nowadays, amplitude-preserving processing is a mandatory task for true-amplitude migration, AVO analysis or 4D interpretation; extracting the modelling part with approximate inverses is then risky. Inversion theory provides us with methods to compute a ‘good’ inverse that will honour the seismic data. Pioneering work by Tarantola (1987) has shown the usefulness of inversion for earthquake location and tomography. Since then inversion has been at the heart of many seismic processing breakthroughs, such as least-squares migration (Nemeth 1996), high-resolution radon transforms (Thorson & Claerbout 1985; Sacchi & Ulrych 1995) or projection filtering (Soubaras 1994; Abma & Claerbout 1995). A very popular method of inversion is the least-squares approach, which can be related to a Bayesian estimation of the model parameters. It is well understood that the inversion in a least-squares sense is very sensitive to the noise level present in the data. By noise, I mean abnormally large or small data components, or outliers which are better described by long-tailed probability density functions (PDFs) as opposed to short-tailed Gaussian PDFs, and coherent noise that the seismic operator is unable to model. The noise will spoil any analysis based on the result of the inversion and affect the amplitude recovery of the input data. From a more statistical point of view, if the residual, which measures the quality of the data fitting, is corrupted by outliers or coherent noise in the data, it will not have independent and identically distributed (IID) components. A more ‘geophysical way’ of saying this is that the residual will not have a white spectrum. In this paper I show how the residual can be whitened when coherent noise is present in the data. Outliers and noise-burst problems are not addressed here. They can be winnowed out by applying, iteratively, a locally re-weighted regression (Wang, White & Pratt 2000). In the first section I review some basics of inverse theory. Then in the following section I introduce two inversion methods that yield white residuals. The first method proposes approximating the noise covariance operators with prediction-error filters (PEFs). The second method handles the coherent noise by introducing a noise modelling operator within the inversion. These methods are tested with field data.
-
Volumes & issues
-
Volume 42 (2024)
-
Volume 41 (2023)
-
Volume 40 (2022)
-
Volume 39 (2021)
-
Volume 38 (2020)
-
Volume 37 (2019)
-
Volume 36 (2018)
-
Volume 35 (2017)
-
Volume 34 (2016)
-
Volume 33 (2015)
-
Volume 32 (2014)
-
Volume 31 (2013)
-
Volume 30 (2012)
-
Volume 29 (2011)
-
Volume 28 (2010)
-
Volume 27 (2009)
-
Volume 26 (2008)
-
Volume 25 (2007)
-
Volume 24 (2006)
-
Volume 23 (2005)
-
Volume 22 (2004)
-
Volume 21 (2003)
-
Volume 20 (2002)
-
Volume 19 (2001)
-
Volume 18 (2000)
-
Volume 17 (1999)
-
Volume 16 (1998)
-
Volume 15 (1997)
-
Volume 14 (1996)
-
Volume 13 (1995)
-
Volume 12 (1994)
-
Volume 11 (1993)
-
Volume 10 (1992)
-
Volume 9 (1991)
-
Volume 8 (1990)
-
Volume 7 (1989)
-
Volume 6 (1988)
-
Volume 5 (1987)
-
Volume 4 (1986)
-
Volume 3 (1985)
-
Volume 2 (1984)
-
Volume 1 (1983)