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EN
Matching pursuit is able to decompose signals adaptively into a series of wavelets and has been widely applied in signal processing of the geophysical felds. Single-channel matching pursuit could not take into account the lateral continuity of seismic traces, and the recent multichannel matching pursuit exploits the lateral coherence as a constraint, which helps to improve the stability of decomposition results. However, atoms searched by multichannel matching pursuit currently are just shared by lateral seismic traces at the same time slicers. The lack of directionality in multichannel search strategies leads to irrationality in dealing with large dip angle seismic traces. Considering that the waveforms of refection events are relatively continuous and similar, an improved multichannel matching pursuit is proposed to realize the directional decomposition of adjacent signals. Based on the principle of seismic refection events tracking and identifcation, directional multichan nel decomposition of seismic traces is realized. The seismic channel to be decomposed is correlated with the time shift of the optimal atom determined by the previous seismic channel. The time position of the maximum correlation indicates the center time of the optimal atom. Optimal atoms identifed by one iteration of multichannel decomposition have the same frequency and phase parameters, diferent center time and amplitude parameters. The center time of the optimal atoms is consistent with seismic refection events. Tests illustrate that the algorithm can successfully reconstruct 2D seismic data without reducing accuracy. Besides, the application of feld data is of great signifcance for reservoir exploration and hydro-carbon interpretation.
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EN
Prediction of petrophysical properties of deep dolomite reservoir using elastic parameter data is challenging and of great uncertainty. Changes in the petrophysical properties generally induce perturbations in elastic properties. Rock-physics model, which plays a role as a bridge between petrophysical properties and elastic properties, determines the accuracy of inversion for petrophysical properties using elastic properties. Different pore structures lead to variations of rock-physics relationships, and in dolomite reservoir, the influence of pore structure on elastic properties is larger than that of petrophysical properties. We first propose a statistical rock-physics model, in which we consider the effect of pore structure on the nonlinear rockphysical relationship between petrophysical properties and elastic properties of dolomite reservoirs. Then, we propose a Bayesian inversion approach of using elastic properties to predict petrophysical properties and use weight factors to address the difference in accuracy of the input elastic properties in the Bayesian inversion framework. Examples illustrate the proposed approach may produce petrophysical properties of high accuracy for deep dolomite reservoirs.
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