LI Qixin1, LUO Yaneng2, ZHANG Sheng3, ZHANG Lu1, YANG Yadi4, HUANG Handong5
1. CNOOC Research Institute, Beijing 100028, China; 2. R&D Center, BGP, CNPC, Zhuozhou, Hebei 072751, China; 3. College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China; 4. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China; 5. College of Geophysics, China University of Petroleum(Beijing), Beijing 102249, China
Abstract:Model-based inversion and sparse spike inversion,with some constraints,are two common algorithms for deterministic inversion.The output is a single solution; thus,it is hard to evaluate its uncertainty.Geostatistical inversion is usually accomplished using geostatistical simulation combined with Markov Chain Monte Carlo; but seismic inversion,log constraints,and stochastic simulation have not been integrated within a uniform theoretical framework.We integrate seismic inversion,log constraints,and geostatistical information within the Bayesian framework to formulate the simultaneous equations which involve logarithmic impedance and log data.Sequential Gaussian simulation is then employed to sufficiently sample the equations.Numerical studies show that our method is better than conventional least-square inversion because the resolution is high,the inversion is constrained by priori statistical data,and a number of impedance realizations could be used for uncertainty evaluation.Compared with sequential Gaussian simulation entirely based on log data,our method uses seismic data as constraints to reduce the uncertainty of inversion.In accordance with field data inversions,Bayesian sequential stochastic inversion is better than model-based inversion and sparse spike inversion in high vertical resolution and feasibility of uncertainty evaluation.
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