An estimate of the amount of geological CO2 storage over the period 1996-2020

Existing centralised databases of industrial-scale CCS report various characteristics including capture capacities but do not specify the amount of CO2 stored from commercial CCS facilities. We review a variety of publicly available sources to estimate the amount of CO2 that has been captured and stored by operational CCS facilities since 1996. We organise these sources into three categories broadly corresponding to the associated degree of legal liability or auditing. Data were found for twenty commercial-scale facilities, indicating a combined capture capacity of 36 MtCO2 per year. Combining data from all three categories suggests that approximately 27 MtCO2 of this was stored in the subsurface in 2019. However, considering only categories 2 and 1 of reporting, storage estimates for 2019 reduce to 25 MtCO2 and 11 MtCO2, respectively. Nearly half of the projects investigated here are reporting injection rates close to their originally proposed capture rate capacity. Our data also show that between 1996 and 2020, 196 Mt of CO2 has been cumulatively stored, combining data for all three categories. The database presented here provides further insight into the factors influencing performances of CCS operations and the data can be used to parameterise energy system models for analysing plausible scaleup trajectories of CCS.

Techno-Economic Aspects of Noble Gases as Monitoring Tracers

A comprehensive monitoring program is an integral part of the safe operation of geological CO2 storage projects. Noble gases can be used as geochemical tracers to detect a CO2 anomaly and identify its origin, since they display unique signatures in the injected CO2 and naturally occurring geological fluids and gases of the storage site complex. In this study, we assess and demonstrate the suitability of noble gases in source identification of CO2 anomalies even when natural variability and analytical uncertainties are considered. Explicitly, injected CO2 becomes distinguishable from shallow fluids (e.g., subsea gas seeps) due to its inheritance of the radiogenic signature (e.g., high He) of deep crustal fluids by equilibration with the formation water. This equilibration also results in the CO2 inheriting a distinct Xe concentration and Xe/noble gas elemental ratios, which enable the CO2 to be differentiated from deep crustal hydrocarbon gases that may be in the vicinity of a storage reservoir. However, the derivation has uncertainties that may make the latter distinction less reliable. These uncertainties would be best and most economically addressed by coinjection of Xe with a distinct isotope ratio into the CO2 stream. However, such a tracer addition would add significant cost to monitoring programs of currently operating storage projects by up to 70% (i.e., from 1 $US/t to 1.7 $US/t).

Relative Permeability: A Critical Parameter in Numerical Simulations of Multiphase Flow in Porous Media

Effective multiphase flow and transport simulations are a critical tool for screening, selection, and operation of geological CO2 storage sites. The relative permeability curve assumed for these simulations can introduce a large source of uncertainty. It significantly impacts forecasts of all aspects of the reservoir simulation, from CO2 trapping efficiency and phase behavior to volumes of oil, water, and gas produced. Careful consideration must be given to this relationship, so a primary goal of this study is to evaluate the impacts on CO2-EOR model forecasts of a wide range of relevant relative permeability curves, from near linear to highly curved. The Farnsworth Unit (FWU) is an active CO2-EOR operation in the Texas Panhandle and the location of our study site. The Morrow ‘B’ Sandstone, a clastic formation composed of medium to coarse sands, is the target storage formation. Results indicate that uncertainty in the relative permeability curve can impart a significant impact on model predictions. Therefore, selecting an appropriate relative permeability curve for the reservoir of interest is critical for CO2-EOR model design. If measured laboratory relative permeability data are not available, it must be considered as a significant source of uncertainty.

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.