Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples
Abstract
The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics and wave propagation models. The main goal of seismic reservoir characterization is to predict the rock and fluid properties given a set of seismic measurements by combining geophysical models and mathematical methods. This modeling challenge is generally formulated as an inverse problem. The most common geophysical inverse problem is seismic (or elastic) inversion i.e., the estimation of elastic properties, such as seismic velocities or impedances, from seismic amplitudes and travel times. The estimation of petrophysical properties, such as porosity, lithology, and fluid saturations, can also be formulated as an inverse problem and is generally referred to as rock physics inversion (or petrophysical) inversion. Several deterministic and probabilistic methods can be applied to solve seismic inversion problems. Deterministic algorithms predict a single solution, which is a best estimate or most likely value of the model variables of interest. In probabilistic algorithms on the other hand, the solution is the probability distribution of the model variables of interest, which can be expressed as a conditional probability density function, or a set of model realizations conditioned on the data. The probabilistic approach provides a quantification of the uncertainty of the solution in…
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