Integrative Approach for Conditioning, Robust Design and Control in the Subsurface

When predicting processes in the subsurface, the need to quantify prediction uncertainty and to perform probabilistic risk assessment is evident. Subsequent challenges include calibration, robust design, monitoring and control. These follow-up tasks have to account for the uncertainty inherent in the system description. Numerical Monte-Carlo simulation is one of the most popular options for stochastic modelling. It is simple and almost universally applicable. Unfortunately, it has vast computational costs that may become strictly prohibitive when joining it with follow-up tasks. Polynomial chaos expansion (PCE) is a promising new approach and is receiving a quickly growing attention, because of its computational efficiency. However, only little work has been done to make PCE available to the above follow-up tasks.

This project will advance and extend PCE, producing a single, integrative and efficient framework for stochastic modelling and its advanced follow-up tasks. In the new framework, all involved quantities will be treated via an integrative response surface that approximates the system’s behavior under all probable states and within the entire feasible range of design or control variables. The computational costs of stochastics and optimization or inversion will no longer multiply.

The drastic gain in computational efficiency will finally allow performing advanced follow-up tasks at their full level of complexity to full-scale real-world problems. This will be demonstrated by application to CO2 injection into the deep subsurface. In this example, site characterization, site selection, design and control of injection strategies, as well as optimal monitoring of CO2 leakage to the surface will be performed on a probabilistic basis, allowing to better quantify, handle and minimize the involved risks.