Multicomponent Processing Research Articles

Approximate separation of pure-mode and converted waves in 3-C reflection seismics by τ-p transform

The use of multicomponent receivers allows one to record the complete elastic wavefield. This is desirable since knowledge of both P- and S-wave characteristics yields better insights into subsurface lithologic and structural rock properties than P-wave knowledge alone. It is often assumed in multicomponent processing that the vertical z-component contains principally pure-mode P-wave arrivals and that the inline horizontal x-component consists mainly of P-SV convertedwave energy. This assumption actually becomes worse with increasing offset. Contaminating energy on either component should ideally be removed to prevent degeneration of stack quality. Often, the x-component is more contaminated than the z-component because P-wave incidence angles (from vertical) are larger than those of P-S waves. This renders processing of the x-component more challenging. Removal of contaminating energy on either component may lead to sharper images. One way of removing such undesired energy is by means of wavefield separation. We propose a simple, approximate wavefield-separation scheme in the τ -p domain to better

Advanced signal processing tools for dispersive waves

ABSTRACTTwo field examples are presented, showing the advantages of using multicomponent sensors for surface‐wave studies. Multicomponent sensors allow the use of specific signal‐processing tools such as the multicomponent singular value decomposition filter and the multicomponent polarization filter, which are both very efficient at separating surface waves from the other waves that comprise a seismic field record.Firstly, some signal‐processing tools for studying surface waves are described. The various filters range from classical to advanced techniques. For processing single‐component data, the filters are the filter and filters based on singular value decomposition and on spectral matrix decomposition. For processing multicomponent data, the filters are the 4C‐singular value decomposition filter and the classical or high‐order polarization filter.Secondly, processing sequences that can be applied to the field data are described and the single‐component processing sequence and the multicomponent processing sequence are compared. Two field examples are presented. The first data set is a land seismic data recording on 2C sensors. The second data set was obtained from a marine acquisition with OBS (4 components). The results obtained illustrate the advantages of using multicomponent filters. The efficiency of the 4C‐SVD filter and the high‐order statistic polarization filter is demonstrated.

Interpretation and practical applications of 4C-3D seismic data, East Cameron gas fields, Gulf of Mexico

Rapid advancements in multicomponent acquisition methods and processing techniques have led to numerous applications for converted wave (C-wave) data that are increasingly used for exploration and exploitation of oil and gas. However, the increased prevalence of multicomponent seismic applications means that interpreters must face many difficult challenges: how to register P-wave time to C-wave time, determine the best methodology for interpreting C-wave data, and/or how to apply the C-wave interpretation in assessing the risks of exploration and exploitation prospects. This paper addresses these issues and attempts to guide the interpreter through the multicomponent data interpretive process with numerous examples from two East Cameron gas fields in the Gulf of Mexico.

Converted‐wave seismic exploration: Applications

Converted seismic waves (specifically, downgoing P‐waves that convert on reflection to upcoming S‐waves are increasingly being used to explore for subsurface targets. Rapid advancements in both land and marine multicomponent acquisition and processing techniques have led to numerous applications for P‐S surveys. Uses that have arisen include structural imaging (e.g., “seeing” through gas‐bearing sediments, improved fault definition, enhanced near‐surface resolution), lithologic estimation (e.g., sand versus shale content, porosity), anisotropy analysis (e.g., fracture density and orientation), subsurface fluid description, and reservoir monitoring. Further applications of P‐S data and analysis of other more complicated converted modes are developing.

Seismic imaging beyond depth migration

If seismic imaging is formulated in terms of two focusing steps—focusing in emission and focusing in detection (or vice versa)—the output of the first focusing step yields a new type of seismic gather, the common‐focus‐point (CFP) gather, which is available for data analysis and information extraction. One important consequence of this novel option is that the involved focusing operators can be updated without updating the underlying velocity model. Introducing the concept of “dynamic focusing,” it is proposed to verify the validity of focusing operators by comparing the “gather of focus‐point responses” with the “gather of focusing operators.” Compared with velocity‐driven time and depth migration, operator‐driven CFP migration can be considered as the most general approach to seismic imaging: it does not require a velocity model, and it automatically takes into account unknown complex propagation effects such as conversion, anisotropy, and dispersion. In addition, in CFP migration, the second focusing step can be extended to produce both angle‐averaged reflection information and angle‐dependent reflection information. The CFP approach to seismic migration allows new solutions in the situation of complex near‐surface layers, subsalt targets, multicomponent processing, and time lapse analysis.

Using refracted shear waves for velocity estimation

The most difficult part of multicomponent processing is the estimation of the shear‐wave velocity map for migration. We used refracted shear waves and a simple iterative method called wavefield continuation (WFC) to evaluate the shallow shear‐wave velocity profile on a real data example. The WFC was developed in 1981 by Clayton and McMechan to determine compressional‐wave velocity profiles from refracted compressional waves. The application to refracted shear waves is straightforward. The real data example shows that shear structure can be easily determined independently of the compressional structure.

Recent advances in multicomponent processing

The evolution of 3-D multicomponent processing and collection methods has increased our information about reservoirs. Processing techniques now exist to generate a time or depth image of P-to-S converted waves (or C-waves) using ocean‐bottom sensors. These images can enhance reservoir characterization, especially when shallow gas clouds may inhibit compressional wave propagation.

Linear matrix operations for multicomponent seismic processing

SUMMARY Multicomponent seismic data contain overlapping information on the polarization states of distinct body-wave modes, due to the physical process of excitation, propagation and recording. This geometric redundancy should be exploited to provide an accurate separation and estimation of the wavefield attributes in order to understand the medium properly. This may be achieved using linear transforms, originally developed for separating split shear waves in four-component seismic data. These transforms separate the principal time-series components of the wavefield from the ray-path geometry and the orientation of the source and geophone axes for a uniform medium: they are deterministic and can be easily implemented. Here we reformulate the linear transforms by introducing simple geometry and medium-independent matrix operators. Although for ‘ideal’ experiments the technique may offer nothing new to the estimation of polarization that eigenanalysis cannot offer, nevertheless the formulation avoids the need to consult mathematical libraries and is useful in the interpretation of the wavefield when various inevitable acquisition-related errors dominate. Some typical problems in processing four-component data, such as the interpretation of data matrix asymmetry due to misorientations of the acquisition components and non-orthogonal polarizations for the wave components, may be easily treated and identified using a common framework with this condensed matrix form. In addition, the operation is extended to similar geometric problems in six- and nine-component data. Synthetic and field nine-component data examples are presented to illustrate the application of the matrix operations.

A new simulation procedure for multicomponent distillation column processing nonideal solutions or reactive solutions.

A powerful new simulation procedure is developed for multicomponent distillation columns processing nonideal solutions or reactive solutions. Although the tridiagonal matrix algorithm is incorporated, the main calculational loop is the Newton-Raphson method in which liquid mole fractions are chosen for the independent variables and the functions to be zeroed are originally defined. The procedure does not vitiate at all such great advantages of the conventional tridiagonal matrix method as the ready incorporation of heat balances, nonideality of the solutions and chemical reactions, and still the algorithm and computer programming are rather simple. Nevertheless, the procedure presents much greater stability in finding converged solutions. A total of eight numerical experiments are made under a variety of conditions, and in all cases rigorous solutions are obtained achieving convergence in three to nine iterations.

Signal processing for magnetic pulsations

Many methods of analysis have been developed in order to study Pc1, Pc3–4 and Pi2. These three classifications are good examples of the great variety of signals. The review is divided into two parts. In the first part, one component signals are studied. We have to determine the number of degrees of freedom for our three magnetic variations. Discussion of the choice of spectral analysis is made in terms of the relative importance of deterministic and random data, and stationarity and time duration of the signal. In the second part, multicomponent signal processing is surveyed. Study of polarization at one point in space or comparison of signal recorded on a network of stations are illustrated. There are many interesting methods in the literature. Instead of a general solution we propose a check list to determine the best and simplest method for magnetic signals. The same classification as in Section 1 is applied this time to the relation between components.