Interest in carbon capture and storage (CCS) is growing rapidly as a crucial part of global efforts to reduce greenhouse gas emissions into the atmosphere. To support this growth in capture technology, we need an acceleration in new CO 2 storage project developments. In this book, we review the science and technology underpinning CO 2 storage in deep saline aquifer formations using insights from several industrial-scale projects. We analyze the main factors which limit storage capacity — constraints governed by flow dynamics, injectivity, pressure development, and geomechanics. Then, this physical basis provides a framework for determining how to optimize monitoring methods. Using the latest portfolio of geophysical methods for smart and cost-effective monitoring at the surface and downhole (including conventional seismic acquisition, passive seismic listening, and fiber-optic sensing), we discuss how short- and long-term storage assurance can be demonstrated with high levels of confidence. Next, we address the question of what is needed to achieve climate-significant scales of CCS deployment. Although technically achievable, the current socio-economic framing often makes storage project execution difficult in practice. By building technical confidence in project execution, we may be able ‘turn the dial’ and realize the gigatonne levels of storage needed over the coming decades.

Abstract Reservoir modelling tools can be invaluable for integrating knowledge and for supporting strategic oil field decisions. The pertinent issue is the capability of the modelling toolbox to achieve the required support: does modelling generate insights into the characterization of the subsurface, does it increase or decrease our working efficiency and does it help or hinder us in decision-making? In this respect, we see two directions emerging in reservoir modelling and simulation. One surrounds software technology development and a move towards a grid-independent world. This is a current research issue but some of the components required to complete a new workflow are already in place and tools for certain specific applications may not be far away. The other involves a change in approach to model design. This involves a move away from big, detailed ‘life-cycle’ models to more nimble workflows involving multi-models (either multi-scale or multi-concept) which may or not include full-field modelling exercises. A distinction between ‘resource models’ and ‘decision models’ helps crystallize this, is a positive step towards achieving ‘fit-for-purpose’ models, and is a change of model design strategy which can be achieved immediately.

Abstract We have used a reservoir gridblock-size outcrop (10 × 100 m) of fluvio- deltaic sandstones to evaluate the importance of internal heterogeneity for a hypothetical waterflood displacement process. Using a dataset based on probe permeameter measurements taken from two vertical transects representing “wells” (5 cm sampling) and one “core” sample (exhaustive 2-mm-spaced sampling), we evaluate the permeability variability at different lengthscales, the correlation characteristics (structure of the variogram function), and importance of volume and data support. We then relate these statistical measures to the sedimentology. We show how the sediment architecture influences the effective tensor permeability at the lamina and bed scales, and then calculate the effective relative permeability functions for a waterflood. We compare the degree of oil recovery from the formation: (1) using averaged borehole data and no geological structure, and (2) modeling the sediment architecture of the interwell volume using mixed stochastic/deterministic methods. We find that the sediment architecture has an important effect on flow performance, mainly due to bed-scale capillary trapping and a consequent reduction in the effective oil mobility. The predicted oil recovery differs by 18% when these small-scale effects are included in the model. Traditional reservoir engineering methods using average permeability values only prove acceptable in high-permeability and low-heterogeneity zones. The main outstanding challenge, represented by this illustration of sub-gridblock scale heterogeneity, is how to capture the relevant geological structure along with the inherent geo-statistical variability. An approach to this problem is proposed.