Copula modeling and uncertainty propagation in field-scale simulation of CO$_2$ fault leakage

TL;DR

This paper introduces a computational framework combining stochastic upscaling and copula-based dependency modeling to efficiently propagate uncertainty in field-scale CO$_2$ storage simulations, reducing computational costs significantly.

Contribution

It presents a novel integration of copula models with adaptive stratified sampling for uncertainty propagation in complex geological CO$_2$ storage scenarios.

Findings

Cost reduction of one to two orders of magnitude compared to standard Monte Carlo methods.

Effective modeling of dependencies between flow variables using copulas.

Accurate capture of non-stationary correlations in upscaled flow functions.

Abstract

Subsurface storage of CO$_2$ is an important means to mitigate climate change, and to investigate the fate of CO$_2$ over several decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges as their effects on storage operations are uncertain due to limited data. In this work, we present a computational framework for forward propagation of uncertainty, including stochastic upscaling and copula representation of flow functions for a CO$_2$ storage site using the Vette fault zone in the Smeaheia formation in the North Sea as a test case. The upscaling method leads to a reduction of the number of stochastic dimensions and the cost of evaluating the reservoir model. A viable model that represents the upscaled data needs to capture dependencies between variables, and allow sampling. Copulas provide representation of dependent multidimensional random variables and a good fit to data, allow fast sampling, and coupling to the forward propagation method via independent uniform random variables. The non-stationary correlation within some of the upscaled flow function are accurately captured by a data-driven transformation model. The uncertainty in upscaled flow functions and other parameters are propagated to uncertain leakage estimates using numerical reservoir simulation of a two-phase system. The expectations of leakage are estimated by an adaptive stratified sampling technique, where samples are sequentially concentrated to regions of the parameter space to greedily maximize variance reduction. We demonstrate cost reduction compared to standard Monte Carlo of one or two orders of magnitude for simpler test cases with only fault and reservoir layer permeabilities assumed uncertain, and factors 2--8 cost reduction for stochastic multi-phase flow properties and more complex stochastic models.