Title of Research:
Stochastic Integration and Inversion of Data in Reservoir Characterization Process
Description of Research :
Extracting detailed earth models, is of great importance in reservoir characterization process. Many methods have been developed to find models that are consistent with the evidences. However, capturing thin bodies in modeling area and their associated uncertainties is what that only stochastic methods are capable of. Stochastic seismic inversion, as a technique of integrating data from different sources with different scales and inverting them to elastic and consequently engineering parameters, utilizes Bayesian theorem to build a high dimensional Posterior PDF that should be sampled by an appropriate sampling method. Markov-Chain Monte Carlo algorithm has been proven as an effective method of sampling from such complex PDFs that yields equiprobable realizations of the reservoir.
