Monte Carlo-Markov Chain stochastic inversion constrained by seismic waveform
ZHOU Shuangshuang1,2, YIN Xingyao1,2, PEI Song1,2, YANG Yaming1,2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China
Abstract：The resolution of seismic stochastic inversion based on geostatistics and logging data is higher than that of conventional deterministic inversion, so the former is quickly and widely used, but it is difficult to improve calculation efficiency and eliminate randomness. This paper proposes a Monte Carlo-Markov Chain (MCMC) stochastic inversion method based on the constraint of seismic waveform. By making full use of the geophysical mapping relationship between seismic data and parameters to be inversed, and a correlation coefficient to guide pseudo ordinary Kriging interpolation to well data according to the similarity of known seismic waveforms, an initial model is established; then the posterior probability density distribution is constructed under the constraints of seismic data and logging data on the Bayesian framework, and the initial model which can indicate seismic waveforms is randomly simulated multiple times by using the Metropolis-Hastings sampling algorithm. The posterior mean value is the optimal solution to the model parameters. This method effectively improves inversion stability and lateral continuity, reduces randomness, effectively weakens the impact of seismic noises on inversion results, and greatly accelerates the convergence of the Markov chain, which effectively improves computing efficiency and estimation accuracy. Applications on model and real data have proved the MCMC stochastic inversion method constrained by seismic waveforms has good noise resistance, can effectively improve inversion accuracy, and are advantageous in identifying thin reservoirs within a tuning scale. It improves both vertical resolution and ho-rizontal resolution.
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