A Hybrid Stochastic-Deterministic Model Calibration Method for CO2 Storage in Geological Formations

Engineers increasingly attract notice to the natural subsurface for very different and possibly competing kinds of applications. On the one hand, the subsurface contains natural resources. On the other hand, it is used for temporary or permanent storage of waste and gas. For all of these competing use types, it is indispensable for our society to assess their performance, limitations, risks and mutual restrictions. The quality of model predictions depends strongly on the quality of the model parameters.

From previous studies it is known that the main prediction errors and uncertainties in simulating processes in the subsurface associated with gas storage, or more general with injection of a fluid, arises from uncertainties in the subsurface structure and related material properties. However, models are required to have predictive power for the future behavior of the reservoirs with increased confidence so that they can be used to provide robust decision support for managing the injection and storage.

This project will develop computationally efficient and reliable method for history matching with application to subsurface CO2 storage. The methods of quantifying uncertainties and parameter sensitivities in history matching can be divided into two classes: (1) statistics/stochastic-based approaches (e.g., in which multiple samples are drawn from conditional distributions) and (2) deterministic optimization-based approaches (e.g., in which a single optimal model is calibrated and some estimates of post-calibration uncertainties are provided). The current project will discuss both the approaches. The goal of this project is a comparison and hybridization of stochastic and optimization-based methods for uncertainty quantification in model calibration and history matching, thus combining the best aspects of both worlds.