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.

[1]  Chi-Keong Goh,et al.  Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction , 2017, Neurocomputing.

[2]  Bing Zhang,et al.  Poststack impedance inversion using improved particle swarm optimization , 2016 .

[3]  Gao Jing.  Quality factor extraction using instantaneous frequency at envelope peak of direct waves of VSP data , 2008 .

[4]  Frank Press,et al.  Earth models obtained by Monte Carlo inversion. , 1968 .

[5]  Jean Virieux,et al.  An overview of full-waveform inversion in exploration geophysics , 2009 .

[6]  Junjie Wu,et al.  Adaptive Differential Evolution by Adjusting Subcomponent Crossover Rate for High-Dimensional Waveform Inversion , 2015, IEEE Geoscience and Remote Sensing Letters.

[7]  K. V. Price,et al.  Differential evolution: a fast and simple numerical optimizer , 1996, Proceedings of North American Fuzzy Information Processing.

[8]  Jinghuai Gao,et al.  Seismic Attenuation Estimation From Instantaneous Frequency , 2010, IEEE Geoscience and Remote Sensing Letters.

[9]  Wang Lan,et al.  Types,characteristics,genesis and prospects of conventional and unconventional hydrocarbon accumulations:taking tight oil and tight gas in China as an instance , 2012 .

[10]  Jinghuai Gao,et al.  A New Highly Efficient Differential Evolution Scheme and Its Application to Waveform Inversion , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  On the Method of Adaptive Waveform Inversion with Zero-Offset VSP Data , 2009 .

[12]  Jinghuai Gao,et al.  Multimutation Differential Evolution Algorithm and Its Application to Seismic Inversion , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jinghuai Gao,et al.  Estimation of Quality Factor Q From the Instantaneous Frequency at the Envelope Peak of a Seismic Signal , 2011 .

[14]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[15]  Shahryar Rahnamayan,et al.  Metaheuristics in large-scale global continues optimization: A survey , 2015, Inf. Sci..

[16]  D. C. Ganley,et al.  A method for calculating synthetic seismograms which include the effects of absorption and dispersion , 1981 .

[17]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Chao Wang,et al.  A new differential evolution algorithm with Cooperative Coevolutionary selection operator for waveform inversion , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Anders Lyhne Christensen,et al.  Novelty-Driven Cooperative Coevolution , 2017, Evolutionary Computation.

[20]  Raj Kumar,et al.  Altimeter-Derived Ocean Wave Period Using Genetic Algorithm , 2011, IEEE Geoscience and Remote Sensing Letters.

[21]  Chao Wang,et al.  High-Dimensional Waveform Inversion With Cooperative Coevolutionary Differential Evolution Algorithm , 2012, IEEE Geoscience and Remote Sensing Letters.