Prediction of CO₂ Saturation Spatial Distribution Using Geostatistical Inversion of Time-Lapse Geophysical Data
Darío Graña, Mingliang Liu, Mohit Ayani
Abstract
Carbon dioxide sequestration in deep saline aquifers and depleted reservoirs relies on numerical models for the prediction of the spatial distribution of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> saturation during injection and migration. Due to the limited knowledge of the rock and fluid properties before injection, model predictions are often uncertain and must be updated when new measurements are available. The spatial distribution of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> saturation and the plume location can be monitored using time-lapse geophysical data, such as seismic and controlled source electromagnetic surveys. We propose a geostatistical inversion approach for the prediction of the time-dependent spatial distribution of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> saturation from geophysical data. The methodology is based on the application of a stochastic optimization method, the Ensemble Smoother, for the solution of the inverse problem, using rock physics and geophysical models. The inversion is applied to the difference in the geophysical data acquired before and during injection. The predicted models of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> saturation are obtained by updating an ensemble of geostatistically generated prior realizations, based on the misfit between geophysical model predictions and measured data. The novelty of the approach is the integration of geostatistical algorithms and stochastic optimization methods for the joint inversion of geophysical data. The proposed approach allows including hydrological constraints in the prior model and quantifying the prediction uncertainty due to the noise and resolution of the data and approximations in the physical relations. The method is applied to the Johansen formation model, offshore Norway, using synthetic seismic and electromagnetic data.