Robust simulation of sharp-interface models for fast estimation of CO2 trapping capacity in large-scale aquifer systems

Abstract

Modeling geological carbon storage represents a new and substantial challenge for the subsurface geosciences. To increase understanding and make good engineering decisions, containment processes and large-scale storage operations must be simulated in a thousand-year perspective. Large differences in spatial and temporal scales make it prohibitively expensive to compute the fate of injected CO 2 using traditional 3D simulators. Instead, accurate forecast can be computed using simplified models that are adapted to the specific setting of the bouyancy-driven migration of the light fluid phase. This paper presents a family of vertically integrated models for studying the combined large-scale and long-term effects of structural, residual, and solubility trapping of CO 2 . The models are based on an assumption of a sharp interface separating CO 2 and brine and can provide a detailed inventory of the injected CO 2 volumes over periods of thousands of years within reasonable computational time. To be compatible with simulation tools used in industry, the models are formulated in a black-oil framework. The models are implemented in MRST-co2lab, which is an open community

A Simulation Workflow for Large-scale CO2 Storage in the Norwegian North Sea

“…In addition, each cell in the grid contains information of the petrophysical properties in the volume that lies below it in the 3D representation of the aquifer. The computational methods that will be discussed in the following can be roughly divided into two classes: (i) methods that do not utilize temporal information to identify the potential for structural trapping [4], and (ii) methods for estimating the outcomes of injection operations in a long-term, large-scale perspective by simulating the combined effects of structural, residual, and solubility trapping in a vertically averaged sense [5,6]. The methods are designed to fit together as part of a multi-fidelity tool-chain supporting the flexibility in resolution required of simulations used for decision support.…”

“…Moreover, we assume that φ and k are constant in the z-direction. The general case with an undulating top surface and non-constant petrophysical parameters is discussed in detail in [5] along with certain geometrical approximations that are tacitly introduced in the following. By integrating (5) from 0 to H in the z-direction we obtain the coarse-scale flow equations:…”

Spill-point analysis and structural trapping capacity in saline aquifers using MRST-co2lab

“…Each model realization describes a large 30 × 60 km sandbox in the shape of an inverted gutter. Fifteen different types of top-surface morphologies were designed by combining three different stratigraphic scenarios-flat deposition, buried beach ridges in a flooded marginal-marine setting (FMM), and buried offshore sand ridges (OSS)-with five different structural scenarios: no faults, uniform (UP) or random (NP) fault displacement and length, and either a single 90 • strike (1) or 30 • and 90 • strike directions (2). Offshore sand ridges with uniform faults and a single strike direction will hence be referred to as OSS UP1, and so on.…”

“…These methods have later been combined with simulation tools based on an assumption of vertical equilibrium [2,3] to provide a comprehensive toolbox for optimizing injection strategies and simulating large-scale containment in a thousand-year perspective [4,5,6].…”

Reexamining CO2 Storage Capacity and Utilization of the Utsira Formation

“…The additional amount trapped by these mechanisms is hard to estimate a priori, but can be obtained from numerical simulation of the whole injection and migration phases. Vertical equilibrium (VE) modeling (Nilsen et al, 2014a) provides us with a tool to carry out simplified long-term simulations rapidly while respecting the most relevant physics. The ability to run multiple rapid simulations enables us to compute optimal injection schedules using nonlinear optimization.…”