Cognitive seismic data modelling based successive differential evolution algorithm for effective exploration of oil-gas reservoirs

Abstract

A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. This algorithm is suitable for the high-dimensional nonseparable model space where the inversion leads to recognition and prediction of hydrocarbon reservoirs. The viscoelastic medium is split into layers whose thicknesses equal to the space between two successive VSP geophones, and the estimated parameters of each layer span the related subspace. All estimated parameters span to a high dimensional nonseparable model space. We develop bottom-up workflow, in which the Q factor and the velocity are estimated using the DE algorithm layer by layer. In order to improve the inversion precision, the crossover strategy is discarded and we derive the weighted mutation strategy. Additionally, two kinds of stopping criteria for effective iteration are proposed to speed up the computation. The new method has fast speed, good convergence and is no longer dependent on the initial values of model parameters. Experimental results on both synthetic and real zero-offset VSP data indicate that this method is noise robust and has great potential to derive reliable seismic attenuation and velocity, which is an important diagnostic tool for reservoir characterization.