Multisource encoding and decoding using the signal apparition technique

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V49-V59 ◽  
Author(s):  
Lasse Amundsen ◽  
Fredrik Andersson ◽  
Dirk-Jan van Manen ◽  
Johan O. A. Robertsson ◽  
Kurt Eggenberger

Signal apparition is a method for encoding sources in simultaneous multisource seismic acquisition and decoding the multisource response of the earth into its single-source responses. For [Formula: see text] sources, encoding is performed by applying periodic sequences of period [Formula: see text] to each of the sources along source lines. Decoding is achieved in the wavenumber domain for each frequency by solving an [Formula: see text] linear system of equations. The system’s matrix is the product of a Fourier matrix and an encoding matrix, the latter containing the information of the codes. The solution of the system is unique when the encoding matrix is invertible. When the encoding sequences consist of time delays applied to sources’ firing times, the determinant of the encoding matrix becomes a polynomial. A unique solution to decoding then exists if the roots of the polynomial avoid the unit circle. Periodic time-shift sequences for two, three, four, and six sources are discussed. A model example of simultaneous four-source data acquisition illustrates the performance of the encoding/decoding technique for the spatially nonaliased case.

1999 ◽  
Vol 31 (01) ◽  
pp. 220-234 ◽  
Author(s):  
Mikael Andersson

A multitype chain-binomial epidemic process is defined for a closed finite population by sampling a simple multidimensional counting process at certain points. The final size of the epidemic is then characterized, given the counting process, as the smallest root of a non-linear system of equations. By letting the population grow, this characterization is used, in combination with a branching process approximation and a weak convergence result for the counting process, to derive the asymptotic distribution of the final size. This is done for processes with an irreducible contact structure both when the initial infection increases at the same rate as the population and when it stays fixed.


2021 ◽  
Author(s):  
Yahan Yang ◽  
Ali Samii ◽  
Zhenlong Zhao ◽  
Guotong Ren

Abstract Despite the rapid rise of computing power and advances in computational techniques in past decades, it is still challenging in reservoir simulation to model complex and detailed features that are represented by small cells with large permeability values, for example, fractures, multi-segment wells, etc. While those features may carry a large amount of flow and thus have a significant impact on the performance prediction, the combination of small volume and large permeability unfortunately leads to well-known time stepping and convergence difficulties during Newton iteration. We address this issue of high flow through small cells by developing a new semi-elimination computational technique. At the beginning of simulation, we construct a set of pressure basis which is a mapping from pressures at surrounding cells in the bulk of reservoir to pressures at those small cells. Next, we start the time-stepping scheme. For each time step or iteration within a time step, small cells are first employed to provide an accurate computation of flow rates and derivatives using upstream weighting and a flow partitioning scheme. Afterwards, small cells are eliminated and a linear system of equations is assembled and solved involving only bulk cells. This semi-elimination technique allows us to fundamentally avoid the drawbacks caused by including small cells in the global system of equations, while capturing their effect on the flow of hydrocarbon in the reservoir. One of the advantages of the proposed techniques over other existing methods is that it is fully implicit and preserves upstream weighting and compositions of the flow field even after small cells are eliminated, which enhances numerical stability and accuracy of simulation results. Application of this technique to several synthetic and field models demonstrates significant performance and accuracy improvement over standard approaches. This method thus offers a practical way to model complex and dynamic flow behaviors in important features without incurring penalties in speed and robustness of the simulation.


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