Markov Chain Stochastic Seismic Inversion for Rock Properties Estimation
Cobos, Carlos 1969-
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Our purpose is to introduce a stochastic seismic inversion algorithm based on the Markov Chain Monte Carlo Simulation. Conventional inversion algorithms generate elastic properties instead of the key rock properties needed to reliably characterize the hydrocarbon potential of a given subsurface interval. In contrast, the inversion scheme presented here generates a set of possible combinations of rock properties that can explain seismic amplitude responses in terms of lithology, pore structure and fluid variations. The result of the probabilistic seismic inversion is a seismic lithofacies catalog that can describe the elastic response of the studied subsurface interval. The main advantage of this technique is that the results consist of many equally probable rock property models as an alternative to multiple elastic property scenarios. Therefore, no post facto elastic-to-rock-properties conversion is needed. The method might be used either in exploratory areas or hydrocarbon field development. In exploratory areas, the stochastic rock physics inversion can support the evaluation for hydrocarbon potential considering the effects of reservoir properties on seismic signatures for different geologic scenarios and physical conditions, with the prime goal of minimizing uncertainties and risk. In field development areas, stochastic seismic inversion produces multiple equally probable rock property models that can explain the real 3D seismic response; it can be used to constrain possible reservoir models used for hydrocarbon reserve estimation and reservoir production simulation. The probabilistic inversion algorithm was successfully tested on a set of real seismic and well-log data to demonstrate the feasibility of the estimation of critical rock properties for hydrocarbon exploration, such as total porosity and reservoir fraction. The real-data test results confirmed the capability of the proposed inversion technique to accurately predict the rock properties of reservoir seismic lithofacies, even for seismically thin layers.