Dynamic subwindow matching: A new similarity measure for seismic facies analysis

Abstract

Seismic facies analysis is usually implemented using pattern recognition methods to classify seismic waveforms extracted along a horizon with a fixed time window. The commonly used similarity measures in those technologies are sensitive to the accuracy of horizon interpretation and the window selection and may thus lead to an unsatisfactory facies map. In this paper, we propose a novel similarity measure called dynamic subwindow matching for seismic facies analysis. The proposed measure is the average similarity of a series of short waveforms containing optimal matching subwindows instead of using the whole signal as done in traditional measures. Because of the introduction of nonlinear alignments for seismic reflection events, the proposed measure has the ability to overcome the horizon inaccuracy problem in seismic facies analysis. Furthermore, it can be directly applied to measure the similarity of the waveforms with different time lengths from the targeted interval. Finally, a new method combining the proposed measure and the k-nearest neighbours method is developed for supervised seismic facies analysis. The synthetic and field data application demonstrate that the proposed measure is robust to amplitude and horizon interpretation noise and provide an effective approach for seismic facies analysis of varying-thickness reservoirs.

Prediction of Shale Gas Pressure based on Multi‐channel Seismic Inversion in Fuling

“…The accuracy of these two methods is highly dependent on an accurate velocity model (Han et al, 1986;Singh et al, 2006). At present, the velocity modeling method for a single well is most commonly obtained by extracting single-channel seismic data from beside the well (Zong et al, 2012;Song et al, 2016;Li et al, 2018). However, with the complication of geological conditions, the requirements for reservoir interpretation accuracy are gradually increasing.…”